This is a Classification task
The dataset presents pet's characteristics and includes tabular, text and image data.
The aim is to predict the rate at which a pet is adopted.
Data fields:
Type - Dog or Cat
Age - Age of pet when listed, in months
Gender - Gender of pet (Male, Female, Mixed, if profile represents group of pets)
Color1 - Color 1 of pet
Color2 - Color 2 of pet
Color3 - Color 3 of pet
MaturitySize - Size at maturity (Small, Medium, Large, Extra Large, Not Specified)
FurLength - Fur length (Short, Medium, Long, Not Specified)
Vaccinated - Pet has been vaccinated (Yes, No, Not Sure)
Dewormed - Pet has been dewormed (Yes, No, Not Sure)
Sterilized - Pet has been spayed / neutered (Yes, No, Not Sure)
Health - Health Condition (Healthy, Minor Injury, Serious Injury, Not Specified)
Fee - Adoption fee (0 = Free)
Breed - breed of pet (see on the dataset)
Description - Profile write-up for this pet. The primary language used is English, with some in Malay or Chinese.
Image - a pointer to an image
The aim is to predic AdoptionSpeed. The value is determined by how quickly, if at all, a pet is adopted. The values are determined in the following way:
0 - Pet was adopted on the same day as it was listed.
1 - Pet was adopted between 1 and 7 days (1st week) after being listed.
2 - Pet was adopted between 8 and 30 days (1st month) after being listed.
3 - Pet was adopted between 31 and 90 days (2nd & 3rd month) after being listed.
4 - No adoption after 100 days of being listed. (There are no pets in this dataset that waited between 90 and 100 days).
Submissions are scored based on the quadratic weighted kappa, which measures the agreement between two ratings. This metrics exist on sklean: sklearn.metrics.cohen_kappa_score with weights="quadratic".
# Importing required libraries and methods
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LinearRegression
from sklearn.compose import make_column_selector as selector
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.impute import SimpleImputer
import numpy as np
import pandas as pd
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.compose import ColumnTransformer
from sklearn import linear_model
from sklearn.linear_model import ElasticNet
from sklearn import svm
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.pipeline import make_pipeline
from sklearn import set_config
from sklearn.metrics import mean_squared_error
import seaborn as sns
import matplotlib.pyplot as plt
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.util import ngrams
from nltk.tokenize import WhitespaceTokenizer
from nltk.stem import WordNetLemmatizer, PorterStemmer
from nltk.stem.porter import PorterStemmer
from imblearn.over_sampling import SMOTE
from nltk.stem import WordNetLemmatizer, PorterStemmer
import re
from sklearn.feature_extraction.text import CountVectorizer
from imblearn.pipeline import Pipeline as ImbPipeline
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.ensemble import GradientBoostingClassifier
import unicodedata
import cv2
from sklearn.cluster import MiniBatchKMeans
#packages
#basics
import os
#modeling
from sklearn.model_selection import train_test_split
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential
from tensorflow.keras.regularizers import l2
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.python.client import device_lib
#visualizations
import seaborn as sns
import matplotlib.pyplot as plt
# Import necessary Libraries
import os
from tqdm import tqdm
import warnings
warnings.filterwarnings("ignore")
import ssl
ssl._create_default_https_context = ssl._create_unverified_context
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# pip install opencv
# Read train files
df_train = pd.read_csv("C:\\Users\\praba\\Documents\\GitHub\\UCA SEMESTER 2 M1\\deeplearning 2\\final project and lab\\drive-download-20240301T144626Z-001\\train.csv")
print("In the dataset they are : ", df_train.shape[0], "train observations")
# df_train.head(1)
In the dataset they are : 9000 train observations
# Read test file
df_test = pd.read_csv("C:\\Users\\praba\\Documents\\GitHub\\UCA SEMESTER 2 M1\\deeplearning 2\\final project and lab\\drive-download-20240301T144626Z-001\\test.csv")
print("In the dataset they are : ", df_test.shape[0], "test observations")
# df_test.head(1)
In the dataset they are : 500 test observations
df_train_copy = df_train.copy()
df_train_copy.head(1)
| Type | Age | Gender | Color1 | Color2 | Color3 | MaturitySize | FurLength | Vaccinated | Dewormed | Sterilized | Health | Fee | Description | AdoptionSpeed | Images | Breed | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Dog | 84.0 | Male | Brown | Cream | Unknown | Small | No | Unknown | Yes | No | Healthy | 0.0 | He is either lost or abandoned. Please contact... | 4.0 | 3b178aa59-5.jpg | Terrier |
df_test_copy = df_test.copy()
df_test_copy.head(1)
| Type | Age | Gender | Color1 | Color2 | Color3 | MaturitySize | FurLength | Vaccinated | Dewormed | Sterilized | Health | Fee | Description | Images | Breed | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Cat | 1.0 | Male | Black | White | Unknown | Small | Yes | No | No | No | Healthy | 0.0 | kitten for adoption, pls call for enquiry, off... | 5df99d229-2.jpg | Domestic_Short_Hair |
img_dir = "C:\\Users\\praba\\Documents\\GitHub\\UCA SEMESTER 2 M1\\deeplearning 2\\final project and lab\\drive-download-20240301T144626Z-001\\train_images_all\\"
test_img_dir = "C:\\Users\\praba\\Documents\\GitHub\\UCA SEMESTER 2 M1\\deeplearning 2\\final project and lab\\drive-download-20240301T144626Z-001\\test_images_all\\"
df_train_copy['Images'] = [img_dir+img for img in df_train_copy['Images']]
df_train_copy['Images'][0]
'C:\\Users\\praba\\Documents\\GitHub\\UCA SEMESTER 2 M1\\deeplearning 2\\final project and lab\\drive-download-20240301T144626Z-001\\train_images_all\\3b178aa59-5.jpg'
df_test_copy['Images'] = [test_img_dir+img for img in df_test_copy['Images']]
df_test_copy['Images'][0]
'C:\\Users\\praba\\Documents\\GitHub\\UCA SEMESTER 2 M1\\deeplearning 2\\final project and lab\\drive-download-20240301T144626Z-001\\test_images_all\\5df99d229-2.jpg'
df_train_copy.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 9000 entries, 0 to 8999 Data columns (total 17 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Type 9000 non-null object 1 Age 9000 non-null float64 2 Gender 9000 non-null object 3 Color1 9000 non-null object 4 Color2 9000 non-null object 5 Color3 9000 non-null object 6 MaturitySize 9000 non-null object 7 FurLength 9000 non-null object 8 Vaccinated 9000 non-null object 9 Dewormed 9000 non-null object 10 Sterilized 9000 non-null object 11 Health 9000 non-null object 12 Fee 9000 non-null float64 13 Description 9000 non-null object 14 AdoptionSpeed 9000 non-null float64 15 Images 9000 non-null object 16 Breed 9000 non-null object dtypes: float64(3), object(14) memory usage: 1.2+ MB
df_test_copy.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 500 entries, 0 to 499 Data columns (total 16 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Type 500 non-null object 1 Age 500 non-null float64 2 Gender 500 non-null object 3 Color1 500 non-null object 4 Color2 500 non-null object 5 Color3 500 non-null object 6 MaturitySize 500 non-null object 7 FurLength 500 non-null object 8 Vaccinated 500 non-null object 9 Dewormed 500 non-null object 10 Sterilized 500 non-null object 11 Health 500 non-null object 12 Fee 500 non-null float64 13 Description 500 non-null object 14 Images 500 non-null object 15 Breed 500 non-null object dtypes: float64(2), object(14) memory usage: 62.6+ KB
Conclusion:
ordered_series = df_train_copy['AdoptionSpeed'].value_counts().sort_index()
print(ordered_series)
AdoptionSpeed 0.0 247 1.0 1894 2.0 2504 3.0 2061 4.0 2294 Name: count, dtype: int64
df_train_copy.Color3.value_counts()
Color3 Unknown 7055 White 1451 Cream 186 Gray 146 Yellow 88 Golden 74 Name: count, dtype: int64
Color3 will be dropped as 7055 data values are unknown out of 9000
# display species graph
plt.subplot(1, 2, 1)
df_train_copy['Type'].value_counts().plot(kind='bar', figsize=(12,5),color=['blue','green'])
plt.title('Species')
plt.xlabel('Count')
# display adoption speed graph
plt.subplot(1, 2, 2)
df_train_copy['AdoptionSpeed'].value_counts().sort_index().plot(kind='bar',color = ["red","yellow",'blue','green','orange'])
plt.title('Adoption Speed')
plt.xlabel('Count')
print(df_train_copy['AdoptionSpeed'].value_counts())
# minimize overlap
plt.tight_layout()
plt.show()
AdoptionSpeed 2.0 2504 4.0 2294 3.0 2061 1.0 1894 0.0 247 Name: count, dtype: int64
#https://towardsdatascience.com/python-plotting-basics-simple-charts-with-matplotlib-seaborn-and-plotly-e36346952a3a
fig, ax = plt.subplots(figsize=(3,4))
plt.rcParams['font.size']=17
#percent count
labels = ["dog","cat"]
percentages = [(df_train_copy[df_train_copy["Type"]=="Dog"].AdoptionSpeed.shape[0]*100)/df_train_copy.shape[0],
(df_train_copy[df_train_copy["Type"]=="Cat"].AdoptionSpeed.shape[0]*100)/df_train_copy.shape[0]]
ax.pie(percentages, labels=labels,
autopct='%1.0f%%',
shadow=False, startangle=0,
pctdistance=0.6,labeldistance=0.8)
ax.axis('equal')
ax.set_title("Cat and Dog distribution")
ax.legend(frameon=False, bbox_to_anchor=(1.5,0.8))
plt.show()
The number of dog pet is higher than the number of cat in the training dataset
# Count observations per class
class_counts = df_train_copy['AdoptionSpeed'].value_counts()
# Display results
print(class_counts)
AdoptionSpeed 2.0 2504 4.0 2294 3.0 2061 1.0 1894 0.0 247 Name: count, dtype: int64
#https://towardsdatascience.com/python-plotting-basics-simple-charts-with-matplotlib-seaborn-and-plotly-e36346952a3a
fig, ax = plt.subplots(figsize=(8,4))
plt.rcParams['font.size']=17
#percent count
labels = ["0","1", "2", "3", "4"]
percentages = [(df_train_copy["AdoptionSpeed"].value_counts()[0]*100)/df_train_copy.shape[0],
(df_train_copy["AdoptionSpeed"].value_counts()[1]*100)/df_train_copy.shape[0],
(df_train_copy["AdoptionSpeed"].value_counts()[2]*100)/df_train_copy.shape[0],
(df_train_copy["AdoptionSpeed"].value_counts()[3]*100)/df_train_copy.shape[0],
(df_train_copy["AdoptionSpeed"].value_counts()[4]*100)/df_train_copy.shape[0]]
ax.pie(percentages, labels=labels,
autopct='%1.0f%%',
shadow=False, startangle=0,
pctdistance=1.2,labeldistance=0.8)
ax.axis('equal')
ax.set_title("AdoptionSpeed class distribution")
ax.legend(frameon=False, bbox_to_anchor=(1.5,0.8))
plt.show()
Conclusion:
Our dataset is imbalanced, let's handle this problem later
adoption_speed = df_train_copy['AdoptionSpeed'].value_counts()
plt.figure(figsize=(12, 5))
sns.countplot(x='AdoptionSpeed', hue='Type', data=df_train_copy,palette = "Set2")
plt.title('Distribution of Adoption Speed for Different Types')
plt.xlabel('Adoption Speed')
plt.ylabel('Count')
plt.show()
This shows that most pet were most not likely adopted the same day they were listed and dogs are more likely to get adopted than cats
sns.barplot(x='AdoptionSpeed', y='Age', data=df_train_copy, estimator='mean')
plt.xlabel('Adoption Speed')
plt.ylabel('Average Age')
plt.title('Average Age vs. Adoption Speed')
plt.show()
print(len(df_train_copy.Age.value_counts()))
df_train_copy.Age.describe()
99
count 9000.000000 mean 11.809778 std 19.405099 min 0.000000 25% 2.000000 50% 4.000000 75% 12.000000 max 255.000000 Name: Age, dtype: float64
import seaborn as sns
import matplotlib.pyplot as plt
fig, axe = plt.subplots()
count_value = df_train_copy["Age"].value_counts()
sns.set_style('darkgrid')
sns.barplot(x=count_value.values[0:10], y=count_value.index[0:10], orient="h", ax=axe)
plt.xlabel("Count")
plt.ylabel("Age")
plt.show()
df_train_copy["Gender"].unique()
array(['Male', 'Female'], dtype=object)
# # gender of pet
import matplotlib.pyplot as plt
train_dog = df_train_copy[df_train_copy['Type'] == "Dog"]
train_cat = df_train_copy[df_train_copy['Type'] == "Cat"]
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 7))
plt.title("Genders of cats and dogs in train dataset")
train_dog['Gender'].value_counts().plot(kind='pie', figsize=(9, 9), ax=ax1, colors=['yellow', 'green'], autopct='%1.1f%%')
train_cat['Gender'].value_counts().plot(kind='pie', figsize=(9, 9), ax=ax2, colors=['yellow', 'green'], autopct='%1.1f%%')
ax1.set_xlabel('DOGS')
ax2.set_xlabel('CATS')
plt.show()
The gender distribution in test data is not bad. Females are greater in cats and dogs however the difference is not huge.
# df_train_copy['Breed'].unique()
df_train_copy['Breed'].value_counts().head(10)
Breed Mixed_Breed 3776 Domestic_Short_Hair 1866 Domestic_Medium_Hair 657 Tabby 204 Siamese 170 Persian 144 Domestic_Long_Hair 143 Shih_Tzu 138 Labrador_Retriever 138 Poodle 130 Name: count, dtype: int64
#train.groupby('Health')['Type'].value_counts().plot(kind='barh')
fig, (ax1,ax2) = plt.subplots(1,2, figsize=(10,5)) # 1 row, 2 columns
plt.title("healf by adoption speed of cats and dogs in TRAINING DATASET")
train_dog.groupby('Health')['AdoptionSpeed'].value_counts().plot(kind='bar',ax=ax1)
train_cat.groupby('Health')['AdoptionSpeed'].value_counts().plot(kind='bar',ax=ax2)
ax1.set_xlabel('DOGS')
ax2.set_xlabel('CATS')
Text(0.5, 0, 'CATS')
Heathy animals are most likely to get adopted
df_train_copy.Fee.describe()
count 9000.000000 mean 24.431333 std 81.575346 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 2000.000000 Name: Fee, dtype: float64
index = np.argsort(list(df_train_copy.Fee.value_counts().index))
#plotting
plt.figure(figsize=(10,5))
plt.plot(df_train_copy.Fee.value_counts().index[index],
np.cumsum(df_train_copy.Fee.value_counts().values[index])/np.sum(df_train_copy.Fee.value_counts().values[index]))
plt.title("Adoption Fee")
plt.ylabel("% of total counts")
plt.xlabel("Adoption Fee")
plt.show()
I am going to separate the numerical and categorical in different lists because later I would need to apply different trasformations to each type of column in the pipeline.
I am not goind to take into account the target, description and images columns because they are not numerical or categorical (technically, the target is categorical but it does not matter as we want to predict it and will not use it as part of our input data).
I do not want ot change the original dataset so I will assign X (input data) and y (target) to different variables.
X = df_train_copy.drop('AdoptionSpeed', axis=1).copy()
y = np.array(df_train_copy['AdoptionSpeed']).reshape(-1,1)
numerical_columns_selector = selector(dtype_exclude=object)
categorical_columns_selector = selector(pattern=r'^(?!.*(Description|Images))',dtype_include=object)
numerical_columns = numerical_columns_selector(X)
categorical_columns = categorical_columns_selector(X)
print("List of numerical columns: ",numerical_columns)
print("List of categorical columns: ", categorical_columns)
List of numerical columns: ['Age', 'Fee'] List of categorical columns: ['Type', 'Gender', 'Color1', 'Color2', 'Color3', 'MaturitySize', 'FurLength', 'Vaccinated', 'Dewormed', 'Sterilized', 'Health', 'Breed']
from sklearn.base import BaseEstimator, TransformerMixin
class TypeConverter(BaseEstimator, TransformerMixin):
def __init__(self):
pass
def fit(self, X, y=None):
return self
def transform(self, X):
X_transformed = X.copy()
X_transformed['Type'] = X_transformed['Type'].map({'Dog': 1, 'Cat': 0})
return X_transformed
# # Create the custom pipeline
# type_pipeline = Pipeline([
# ('type_converter', TypeConverter())
# ])
# # Example usage:
# # Assuming df is your DataFrame with 'Gender' column
# transformed_df = type_pipeline.fit(X)
# type_pipeline.transform(X)
Dogs age at a rate of one-fifth that of humans, while cats age at a rate of one-seventh that of humans. I applied this approach to make their ages comparable to those of humans
from sklearn.base import BaseEstimator, TransformerMixin
class AgeConverter(BaseEstimator, TransformerMixin):
def __init__(self, cat_scale=5, dog_scale=7):
self.cat_scale = cat_scale
self.dog_scale = dog_scale
def fit(self, X, y=None):
return self
def transform(self, X):
X_transformed = X.copy()
# Apply the age conversion for cats
X_transformed.loc[X_transformed['Type'] == 'Cat', 'Age'] = \
X_transformed.loc[X_transformed['Type'] == 'Cat', 'Age'] / self.cat_scale
# Apply the age conversion for dogs
X_transformed.loc[X_transformed['Type'] == 'Dog', 'Age'] = \
X_transformed.loc[X_transformed['Type'] == 'Dog', 'Age'] / self.dog_scale
return X_transformed
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.pipeline import Pipeline
import pandas as pd
class GenderConverter(BaseEstimator, TransformerMixin):
def __init__(self):
pass
def fit(self, X, y=None):
return self
def transform(self, X):
X_transformed = X.copy()
X_transformed['Gender'] = X_transformed['Gender'].map({'Male': 1, 'Female': 0})
return X_transformed
# # Create the custom pipeline
# gender_pipeline = Pipeline([
# ('gender_converter', GenderConverter())
# ])
# # Example usage:
# # Assuming df is your DataFrame with 'Gender' column
# transformed_df = gender_pipeline.transform(X)
Ordinal encoding, is a process of transforming categorical variables into numerical variables while preserving the order or hierarchy of the categories. We can apply it to columns of our dataset which are categorical at a first glance but which some kind of progression can be identified in the unique values of the column (such as a progression in size, age, etc).
To identify this, we need to study our columns' unique values.
The column MaturitySize is ordinal (there is a progression from small to extra large in its values), so we can map it as an ordinal column.
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.pipeline import Pipeline
import pandas as pd
class MaturitySizeConverter(BaseEstimator, TransformerMixin):
def __init__(self):
pass
def fit(self, X, y=None):
return self
def transform(self, X):
X_transformed = X.copy()
size_mapping = {'Small': 0, 'Medium': 1, 'Large': 2, 'Extra Large': 3}
X_transformed['MaturitySize'] = X_transformed['MaturitySize'].map(size_mapping)
return X_transformed
# Create the custom pipeline
maturity_size_pipeline = Pipeline([
('maturity_size_converter', MaturitySizeConverter())
])
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import accuracy_score, classification_report
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer
from sklearn.pipeline import make_pipeline
import nltk
import emoji
import string
# from spellchecker import SpellChecker
# from textblob import TextBlob
from nltk.stem import WordNetLemmatizer
# nltk.download('stopwords')
# nltk.download('punkt')
# nltk.download('wordnet')
# nltk.download('omw-1.4')
# stops = set(stopwords.words('english'))
# emoji handling
# !pip install emoji
import re
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.pipeline import Pipeline
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from nltk.tokenize import word_tokenize
import re
import pandas as pd
class TextCleaner(BaseEstimator, TransformerMixin):
def __init__(self):
pass
def fit(self, X, y=None):
return self
def transform(self, X):
X_transformed = X.copy()
X_transformed['Description'] = X_transformed['Description'].apply(self.clean_text1)
return X_transformed
def clean_text1(self, text):
stop_words = set(stopwords.words('english'))
# Initialize the WordNetLemmatizer
lemmatizer = WordNetLemmatizer()
# Remove emojis
emoji_pattern = re.compile("["
u"\U0001F600-\U0001F64F" # emoticons
u"\U0001F300-\U0001F5FF" # symbols & pictographs
u"\U0001F680-\U0001F6FF" # transport & map symbols
u"\U0001F1E0-\U0001F1FF" # flags (iOS)
u"\U0001F700-\U0001F77F" # alchemical symbols
u"\U0001F780-\U0001F7FF" # Geometric Shapes Extended
u"\U0001F800-\U0001F8FF" # Supplemental Arrows-C
u"\U0001F900-\U0001F9FF" # Supplemental Symbols and Pictographs
u"\U0001FA00-\U0001FA6F" # Chess Symbols
u"\U0001FA70-\U0001FAFF" # Symbols and Pictographs Extended-A
u"\U00002702-\U000027B0" # Dingbats
u"\U000024C2-\U0001F251"
u"\U0001F910-\U0001F97A"
u"\U0001F980-\U0001F9B0"
u"\U0001F9C0-\U0001F9FF"
u"\U0001FA60-\U0001FA6F"
u"\U0001FA70-\U0001FAFF"
u"\U00002600-\U000026FF" # Miscellaneous Symbols
u"\U00002700-\U000027BF" # Dingbats
u"\U00002B50"
u"\U000023E9-\U000023FA" # Miscellaneous Technical
"]+", flags=re.UNICODE)
text = emoji_pattern.sub(r'', text)
# Tokenize the text
text_tokens = word_tokenize(text)
# Lemmatize the tokens and remove stop words
lemmatized_text = [lemmatizer.lemmatize(word.lower()) for word in text_tokens if word.lower() not in stop_words]
# Join the lemmatized tokens back into a sentence
cleaned_sentence = ' '.join(lemmatized_text)
# Remove punctuation and extra whitespace
cleaned_sentence = re.sub(r'[^\w\s]', '', cleaned_sentence).strip()
return cleaned_sentence
# Read the first image of the list
from skimage import io
img = io.imread(X['Images'][10])
# have a look to the image
plt.imshow(img)
<matplotlib.image.AxesImage at 0x112fb1a0650>
# !pip install opencv-python
# !pip install opencv-contrib-python
# convert the image to grey levels
import cv2
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# compute SIFT detector and descriptors
sift = cv2.SIFT_create()
kp,des = sift.detectAndCompute(gray,None)
# plot image and descriptors
cv2.drawKeypoints(img,kp,img,flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
plt.imshow(img)
<matplotlib.image.AxesImage at 0x112f6b23150>
from sklearn.base import BaseEstimator,TransformerMixin
import cv2
from sklearn.cluster import MiniBatchKMeans
SIFT (Scale-Invariant Feature Transform) is a feature extraction technique used in computer vision and image processing for detecting and describing local features in images. SIFT features are useful for a wide range of applications such as object recognition, image registration, 3D reconstruction, and image retrieval.
This code performs the following steps:
Extracts Scale-Invariant Feature Transform (SIFT) features from a list of images and returns a list of these features.
Builds a clusterizer (a clustering algorithm) using the list of extracted SIFT features and a desired number of clusters. The clusterizer is then fitted to the SIFT features and returned.
Constructs a Bag of Features (BOF) representation using the list of SIFT features and the fitted clusterizer. The BOF representation is a histogram of the frequency of each cluster (i.e., visual word) in the SIFT features of an image.
import cv2
def extract_SIFT(img_lst):
sift = sift = cv2.SIFT_create()
sift_lst = []
for img in img_lst:
img=cv2.imread(img) #Load images
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #Convert to gray scale
kp, des = sift.detectAndCompute(gray, None) #Extract SIFT (Scale-Invariant Feature Transform) keypoints and descriptors
sift_lst.append(des)
return sift_lst
from sklearn.cluster import KMeans
def clusterize(SIFs, nb_cluster):
sift_features = np.vstack(SIFs)#Stack the SIFT features
clusterizer = KMeans(n_clusters=nb_cluster)# Create the KMeans clusterizer
clusterizer.fit(sift_features)# Fit the clusterizer with the SIFT features
return clusterizer
def build_BOFs(SIFTs, clusterizer):
#Initialize the BOF representation
bof_representation = np.zeros((len(SIFTs), clusterizer.n_clusters), dtype=np.float32)
#Loop through the SIFT features
for i, sift in enumerate(SIFTs):
cluster_labels = clusterizer.predict(sift) #Predict the cluster labels for the SIFT features
histogram = np.bincount(cluster_labels, minlength=clusterizer.n_clusters) #Create a histogram of cluster labels
histogram = histogram / histogram.sum() #Normalize the histogram
bof_representation[i, :] = histogram #Store the histogram in the BOF representation
return bof_representation
from sklearn.base import BaseEstimator
from sklearn.base import TransformerMixin
class BOF_extractor(BaseEstimator,TransformerMixin): # Initialise class MyImageTransformer and nb of clusters
def __init__(self, nb_cluster=4):
self.nb_cluster=4
def fit(self, X, y=None): #Use function extract_SIFT(X) and clusterize(SIFTs, self.nb_cluster) to the input data
self.SIFTs = extract_SIFT(X)
self.clusterizer = clusterize(SIFTs, self.nb_cluster)
def transform(self, X, y=None): #transform input images and return bof representation using build_BOFs()
self.SIFTs = extract_SIFT(X)
return build_BOFs(self.SIFTs, self.clusterizer)
def fit_transform(self, X, y=None): #transform and fit the data using all the previously defined function
self.SIFTs = extract_SIFT(X)
self.clusterizer = clusterize(self.SIFTs, self.nb_cluster)
return build_BOFs(self.SIFTs, self.clusterizer)
To achieve accurate machine learning results, it's important to use a balanced dataset. This means that the number of samples for each class in the dataset should be roughly the same. If the dataset is imbalanced, the machine learning model may be biased towards the majority class, leading to poor performance on the minority class.
To address this issue, my approach is to balance the dataset using a technique like RandomOverSampler.
Performing cross-validation is a powerful method to evaluate the performance of a machine learning model. However, it can be computationally expensive, especially when the dataset is large. In this case, In my case I am facing a time constraint, and the computer takes a long time to perform cross-validation.
To optimize my time, I am planning to only perform cross-validation on the balanced dataset since this is the recommended approach. I will not perform cross-validation on the non-balanced dataset because I only want to compare its accuracy with the accuracy of the balanced dataset
#spliting the data into dependent and independent varaible
y = df_train_copy['AdoptionSpeed']
X = df_train_copy.drop(['AdoptionSpeed'], axis=1).copy()
# Split the data into train and validation set
from sklearn.model_selection import train_test_split
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.20, random_state=2022,stratify=y)
# y
numerical_columns
['Age', 'Fee']
categorical_columns
['Type', 'Gender', 'Color1', 'Color2', 'Color3', 'MaturitySize', 'FurLength', 'Vaccinated', 'Dewormed', 'Sterilized', 'Health', 'Breed']
import pandas as pd
features = X.columns.tolist()
# List comprehension to separate numerical and categorical features
# For numerical features, we include those whose dtype is not 'object',
# and also specifically include 'MaturitySize', 'Gender', and 'Type' columns
numerical_features = [feature for feature in features if
X[feature].dtype != 'object' or feature == 'MaturitySize' or feature == 'Gender' or feature == "Type"]
# For categorical features, we include those whose dtype is 'object'
# but exclude certain columns like 'MaturitySize', 'Gender', 'Description', 'Images','Color3' , 'Type'.
categorical_features = [feature for feature in features if
X[feature].dtype == object and feature != "MaturitySize" and feature != "Gender" and feature != "Description"
and feature != "Images" and feature != "Color3" and feature != "Type" ]
print(numerical_features)
# print("\n")
print(categorical_features)
['Type', 'Age', 'Gender', 'MaturitySize', 'Fee'] ['Color1', 'Color2', 'FurLength', 'Vaccinated', 'Dewormed', 'Sterilized', 'Health', 'Breed']
text_features = ['Description']
image_features = ['Images']
drop_features = ["Color3"]
# checking the number of observations per classes
y.value_counts()
AdoptionSpeed 2.0 2504 4.0 2294 3.0 2061 1.0 1894 0.0 247 Name: count, dtype: int64
# checking the number of observations per classes
y_train.value_counts()
AdoptionSpeed 2.0 2003 4.0 1835 3.0 1649 1.0 1515 0.0 198 Name: count, dtype: int64
y_train.shape
(7200,)
from imblearn.pipeline import Pipeline as ImbPipeline
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.utils import resample
# Creating pipelines for numerical and categorical features
numerical_pipeline = Pipeline(steps=[
('stdscaler', StandardScaler())
])
categorical_pipeline = Pipeline(steps=[
('onehot', OneHotEncoder(handle_unknown='ignore')) # One-hot encoding categorical features
])
# Define pipeline for text features
text_transformer_pipeline = Pipeline(steps=[ ('vectorizer', CountVectorizer())])
image_transformer_pipeline = Pipeline(steps=[ ('img', BOF_extractor())])
col_transformer = ColumnTransformer(transformers=[
('drop_columns','drop',drop_features),
('numerical', numerical_pipeline, numerical_features),
('categorical', categorical_pipeline, categorical_features),
('text-data', text_transformer_pipeline, 'Description'),
('image-data', image_transformer_pipeline, 'Images'),
])
my_sklearn_pipeline = Pipeline(steps=[
('TypeConverter', TypeConverter()),
('age', AgeConverter()),
('text_cleaner', TextCleaner()),
('MaturitySizeConverter' ,MaturitySizeConverter()),
("GenderConverter",GenderConverter()),
('col_transformer',col_transformer),
]
)
my_sklearn_pipeline
Pipeline(steps=[('TypeConverter', TypeConverter()), ('age', AgeConverter()),
('text_cleaner', TextCleaner()),
('MaturitySizeConverter', MaturitySizeConverter()),
('GenderConverter', GenderConverter()),
('col_transformer',
ColumnTransformer(transformers=[('drop_columns', 'drop',
['Color3']),
('numerical',
Pipeline(steps=[('stdscaler',
StandardScaler())]),
['Type', 'A...Gender',
'MaturitySize', 'Fee']),
('categorical',
Pipeline(steps=[('onehot',
OneHotEncoder(handle_unknown='ignore'))]),
['Color1', 'Color2',
'FurLength', 'Vaccinated',
'Dewormed', 'Sterilized',
'Health', 'Breed']),
('text-data',
Pipeline(steps=[('vectorizer',
CountVectorizer())]),
'Description'),
('image-data',
Pipeline(steps=[('img',
BOF_extractor())]),
'Images')]))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(steps=[('TypeConverter', TypeConverter()), ('age', AgeConverter()),
('text_cleaner', TextCleaner()),
('MaturitySizeConverter', MaturitySizeConverter()),
('GenderConverter', GenderConverter()),
('col_transformer',
ColumnTransformer(transformers=[('drop_columns', 'drop',
['Color3']),
('numerical',
Pipeline(steps=[('stdscaler',
StandardScaler())]),
['Type', 'A...Gender',
'MaturitySize', 'Fee']),
('categorical',
Pipeline(steps=[('onehot',
OneHotEncoder(handle_unknown='ignore'))]),
['Color1', 'Color2',
'FurLength', 'Vaccinated',
'Dewormed', 'Sterilized',
'Health', 'Breed']),
('text-data',
Pipeline(steps=[('vectorizer',
CountVectorizer())]),
'Description'),
('image-data',
Pipeline(steps=[('img',
BOF_extractor())]),
'Images')]))])TypeConverter()
AgeConverter()
TextCleaner()
MaturitySizeConverter()
GenderConverter()
ColumnTransformer(transformers=[('drop_columns', 'drop', ['Color3']),
('numerical',
Pipeline(steps=[('stdscaler',
StandardScaler())]),
['Type', 'Age', 'Gender', 'MaturitySize',
'Fee']),
('categorical',
Pipeline(steps=[('onehot',
OneHotEncoder(handle_unknown='ignore'))]),
['Color1', 'Color2', 'FurLength', 'Vaccinated',
'Dewormed', 'Sterilized', 'Health',
'Breed']),
('text-data',
Pipeline(steps=[('vectorizer',
CountVectorizer())]),
'Description'),
('image-data',
Pipeline(steps=[('img', BOF_extractor())]),
'Images')])['Color3']
drop
['Type', 'Age', 'Gender', 'MaturitySize', 'Fee']
StandardScaler()
['Color1', 'Color2', 'FurLength', 'Vaccinated', 'Dewormed', 'Sterilized', 'Health', 'Breed']
OneHotEncoder(handle_unknown='ignore')
Description
CountVectorizer()
Images
BOF_extractor()
# fits the sklearn pipeline `my_sklearn_pipeline` to the training data X_train.
my_sklearn_pipeline.fit(X_train)
Pipeline(steps=[('TypeConverter', TypeConverter()), ('age', AgeConverter()),
('text_cleaner', TextCleaner()),
('MaturitySizeConverter', MaturitySizeConverter()),
('GenderConverter', GenderConverter()),
('col_transformer',
ColumnTransformer(transformers=[('drop_columns', 'drop',
['Color3']),
('numerical',
Pipeline(steps=[('stdscaler',
StandardScaler())]),
['Type', 'A...Gender',
'MaturitySize', 'Fee']),
('categorical',
Pipeline(steps=[('onehot',
OneHotEncoder(handle_unknown='ignore'))]),
['Color1', 'Color2',
'FurLength', 'Vaccinated',
'Dewormed', 'Sterilized',
'Health', 'Breed']),
('text-data',
Pipeline(steps=[('vectorizer',
CountVectorizer())]),
'Description'),
('image-data',
Pipeline(steps=[('img',
BOF_extractor())]),
'Images')]))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(steps=[('TypeConverter', TypeConverter()), ('age', AgeConverter()),
('text_cleaner', TextCleaner()),
('MaturitySizeConverter', MaturitySizeConverter()),
('GenderConverter', GenderConverter()),
('col_transformer',
ColumnTransformer(transformers=[('drop_columns', 'drop',
['Color3']),
('numerical',
Pipeline(steps=[('stdscaler',
StandardScaler())]),
['Type', 'A...Gender',
'MaturitySize', 'Fee']),
('categorical',
Pipeline(steps=[('onehot',
OneHotEncoder(handle_unknown='ignore'))]),
['Color1', 'Color2',
'FurLength', 'Vaccinated',
'Dewormed', 'Sterilized',
'Health', 'Breed']),
('text-data',
Pipeline(steps=[('vectorizer',
CountVectorizer())]),
'Description'),
('image-data',
Pipeline(steps=[('img',
BOF_extractor())]),
'Images')]))])TypeConverter()
AgeConverter()
TextCleaner()
MaturitySizeConverter()
GenderConverter()
ColumnTransformer(transformers=[('drop_columns', 'drop', ['Color3']),
('numerical',
Pipeline(steps=[('stdscaler',
StandardScaler())]),
['Type', 'Age', 'Gender', 'MaturitySize',
'Fee']),
('categorical',
Pipeline(steps=[('onehot',
OneHotEncoder(handle_unknown='ignore'))]),
['Color1', 'Color2', 'FurLength', 'Vaccinated',
'Dewormed', 'Sterilized', 'Health',
'Breed']),
('text-data',
Pipeline(steps=[('vectorizer',
CountVectorizer())]),
'Description'),
('image-data',
Pipeline(steps=[('img', BOF_extractor())]),
'Images')])['Color3']
drop
['Type', 'Age', 'Gender', 'MaturitySize', 'Fee']
StandardScaler()
['Color1', 'Color2', 'FurLength', 'Vaccinated', 'Dewormed', 'Sterilized', 'Health', 'Breed']
OneHotEncoder(handle_unknown='ignore')
Description
CountVectorizer()
Images
BOF_extractor()
# transform the the training data X_train.
X_train_transformed = my_sklearn_pipeline.transform(X_train)
# transform the the validation data X_val.
X_val_transformed = my_sklearn_pipeline.transform(X_val)
RandomOverSampler is used on X_train and y_train to balance the training data.
from imblearn.over_sampling import RandomOverSampler
# desired number of samples for the minority class
desired_minority_samples = 800
minority_class_label = 0.0
# Create a dictionary with the desired sampling strategy
sampling_strategy = {minority_class_label : desired_minority_samples}
# RandomOverSampler with the specified sampling strategy
ros = RandomOverSampler(sampling_strategy=sampling_strategy)
X_train_transformed_resampled, y_train_resampled = ros.fit_resample(X_train_transformed, y_train)
ax = y_train_resampled.value_counts().plot.pie(autopct='%.2f')
_ = ax.set_title("Over-sampling")
y_train_resampled.value_counts()
AdoptionSpeed 2.0 2003 4.0 1835 3.0 1649 1.0 1515 0.0 800 Name: count, dtype: int64
I will use this resampled data set for my model training
X_train_transformed_resampled
<7802x14653 sparse matrix of type '<class 'numpy.float64'>' with 361217 stored elements in Compressed Sparse Row format>
X_val_transformed
<1800x14653 sparse matrix of type '<class 'numpy.float64'>' with 83106 stored elements in Compressed Sparse Row format>
X_train_transformed_resampled is converted into a dataframe
import pandas as pd
dense_array_X_train_transformed_resampled = X_train_transformed_resampled.toarray()
df_X_train_transformed_resampled = pd.DataFrame(dense_array)
df_X_train_transformed_resampled.head(2)
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | 14643 | 14644 | 14645 | 14646 | 14647 | 14648 | 14649 | 14650 | 14651 | 14652 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -1.226519 | 0.101380 | 1.140316 | 0.207758 | -0.310264 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.298734 | 0.124051 | 0.281013 | 0.296203 |
| 1 | -1.226519 | 1.220209 | -0.876950 | 2.036438 | -0.310264 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.471074 | 0.041322 | 0.247934 | 0.239669 |
2 rows × 14653 columns
target class is marged with the X_train_transformed_resampled dataframe
import pandas as pd
# Create a copy of the DataFrame
new_df_train = df_X_train_transformed_resampled.copy()
# Add the Series as a new column to the new DataFrame
new_df_train['AdoptionSpeed'] = y_train_resampled
new_df_train.head(2)
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | 14644 | 14645 | 14646 | 14647 | 14648 | 14649 | 14650 | 14651 | 14652 | AdoptionSpeed | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -1.226519 | 0.101380 | 1.140316 | 0.207758 | -0.310264 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.298734 | 0.124051 | 0.281013 | 0.296203 | 4.0 |
| 1 | -1.226519 | 1.220209 | -0.876950 | 2.036438 | -0.310264 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.471074 | 0.041322 | 0.247934 | 0.239669 | 2.0 |
2 rows × 14654 columns
X_val_transformed is converted into a dataframe
import pandas as pd
dense_array_val_X_val_transformed = X_val_transformed.toarray()
df_val_X_val_transformed = pd.DataFrame(dense_array_val_X_val_transformed)
df_val_X_val_transformed.head(2)
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | 14643 | 14644 | 14645 | 14646 | 14647 | 14648 | 14649 | 14650 | 14651 | 14652 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -1.226519 | -0.559747 | 1.140316 | -1.620921 | -0.310264 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.285714 | 0.069426 | 0.353805 | 0.291055 |
| 1 | 0.815316 | -0.051188 | -0.876950 | 0.207758 | 2.196528 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.177449 | 0.052372 | 0.411583 | 0.358595 |
2 rows × 14653 columns
target class is marged with the X_val_transformed dataframe
import pandas as pd
# Create a copy of the DataFrame
new_df_validation = df_val_X_val_transformed.copy()
# Add the Series as a new column to the new DataFrame
new_df_validation['AdoptionSpeed'] = y_val
new_df_validation.head(2)
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | 14644 | 14645 | 14646 | 14647 | 14648 | 14649 | 14650 | 14651 | 14652 | AdoptionSpeed | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -1.226519 | -0.559747 | 1.140316 | -1.620921 | -0.310264 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.285714 | 0.069426 | 0.353805 | 0.291055 | NaN |
| 1 | 0.815316 | -0.051188 | -0.876950 | 0.207758 | 2.196528 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.177449 | 0.052372 | 0.411583 | 0.358595 | 3.0 |
2 rows × 14654 columns
As my laptop is not capable to process this big data so I have stored the transformed X_train and
X_train_transformed as df dataframe is stored in X_train_transformed_df.csv
# df_X_train_transformed_resampled.to_csv('X_train_transformed_resampled.csv')
# df_X_train_transformed_resampled = pd.read_csv('X_train_transformed_resampled.csv')
X_val_transformed as df_val dataframe is stored in X_val_transformed_df.csv
# df_val_X_val_transformed.to_csv('X_val_transformed.csv')
# df_val_X_val_transformed = pd.read_csv('X_val_transformed.csv')
unique_classes = new_df_train['AdoptionSpeed'].unique() # Identify unique classes
selected_df = pd.DataFrame(columns=new_df_train.columns) # empty dataframe to store selected data
# Sample 200 data points from each class
for class_label in unique_classes:
class_data = new_df_train[new_df_train['AdoptionSpeed'] == class_label]
if len(class_data) < 400:
selected_df = pd.concat([selected_df, class_data])
else:
sampled_data = class_data.sample(n=400, replace=False, random_state=42)
selected_df = pd.concat([selected_df, sampled_data])
# Reset index of the selected dataframe
selected_df.reset_index(drop=True, inplace=True)
selected_df.head(2)
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | 14644 | 14645 | 14646 | 14647 | 14648 | 14649 | 14650 | 14651 | 14652 | AdoptionSpeed | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.815316 | 1.830480 | 1.140316 | -1.620921 | -0.310264 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.225670 | 0.131171 | 0.377997 | 0.265162 | 4.0 |
| 1 | -1.226519 | 0.609939 | 1.140316 | 0.207758 | -0.310264 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.330065 | 0.163399 | 0.196078 | 0.310458 | 4.0 |
2 rows × 14654 columns
selected_df['AdoptionSpeed'].value_counts()
AdoptionSpeed 4.0 400 2.0 400 1.0 400 3.0 400 0.0 400 Name: count, dtype: int64
# Separate features (X_resampled) and target variable (y_resampled)
X_selected_400 = selected_df.drop(columns=['AdoptionSpeed'])
y_selected_400 = selected_df['AdoptionSpeed']
The RandomizedSearchCV I have done only with the balanced dataset, I haven't try even with the non balanced dataset because in my computer it takes more than one day and sometimes it froze and I needed to switch off the computer and start again.
Cross-validation with RandomizedSearchCV is a technique used to optimize the hyperparameters of a machine learning model using randomized search. RandomizedSearchCV is a function from the scikit-learn library that performs hyperparameter tuning using a combination of random search and cross-validation.
The process works by defining a range of hyperparameters and their potential values. RandomizedSearchCV then randomly selects a combination of hyperparameter values from this range and trains and evaluates the model using cross-validation. The process is repeated multiple times, each time selecting a different set of hyperparameter values.
The advantage of RandomizedSearchCV is that it can be more efficient than an exhaustive grid search of all possible hyperparameter combinations. By randomly sampling the hyperparameter space, RandomizedSearchCV can often find good hyperparameter values with fewer evaluations.
The output of RandomizedSearchCV is the best set of hyperparameters that was found during the search, along with the corresponding cross-validation score. These hyperparameters can then be used to train the final model on the entire dataset.
from sklearn.model_selection import RandomizedSearchCV
from sklearn.pipeline import make_pipeline
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
# hyperparameters
param_distributions = {
'RandomForestClassifier': {
# Random Forest hyperparameters
'randomforestclassifier__n_estimators': [20, 50, 150], # Number of trees in the forest
'randomforestclassifier__max_depth': [15, 50, 100], # Maximum depth of the tree
'randomforestclassifier__bootstrap': [True, False], # Whether bootstrap samples are used when building trees
'randomforestclassifier__min_samples_split': [2, 5, 10], # Minimum number of samples required to split a node
'randomforestclassifier__min_samples_leaf': [1, 2, 4], # Minimum number of samples required to be at a leaf node
'randomforestclassifier__max_features': ['auto', 'sqrt', 'log2'] # Number of features to consider when looking for the best split
},
'GradientBoostingClassifier': {
# Gradient Boosting hyperparameters
'gradientboostingclassifier__n_estimators': [80, 100, 120], # Number of boosting stages to be run
'gradientboostingclassifier__learning_rate': [0.1, 0.2, 0.3], # Learning rate shrinks the contribution of each tree
},
'KNN_CLF': {
# KNN hyperparameters
'kneighborsclassifier__n_neighbors': [4, 5, 6], # Number of neighbors to use
'kneighborsclassifier__weights': ['uniform', 'distance'], # Weight function used in prediction
},
'LogisticRegression': {
# Logistic Regression hyperparameters
'logisticregression__penalty': ['l1', 'l2', 'elasticnet'], # Regularization penalty
'logisticregression__C': [0.2, 0.5, 1.0, 2.0,3.0,4.0], # Inverse of regularization strength
'logisticregression__verbose': [1, 2, 3], # Verbosity mode
'logisticregression__max_iter': [5000], # Maximum number of iterations
'logisticregression__solver': ['liblinear', 'saga'] # Algorithm to use in the optimization problem
}
}
# Initialize classifiers
# classifiers = {
# 'GradientBoostingClassifier': make_pipeline(my_sklearn_pipeline, GradientBoostingClassifier()),
# 'KNN_CLF': make_pipeline(my_sklearn_pipeline, KNeighborsClassifier()),
# 'LogisticRegression': make_pipeline(my_sklearn_pipeline, LogisticRegression()),
# 'RandomForestClassifier': make_pipeline(my_sklearn_pipeline, RandomForestClassifier())
# }
classifiers = {
'GradientBoostingClassifier': make_pipeline( GradientBoostingClassifier()),
'KNN_CLF': make_pipeline( KNeighborsClassifier()),
'LogisticRegression': make_pipeline( LogisticRegression()),
'RandomForestClassifier': make_pipeline( RandomForestClassifier())
}
classifiers
{'GradientBoostingClassifier': Pipeline(steps=[('gradientboostingclassifier', GradientBoostingClassifier())]),
'KNN_CLF': Pipeline(steps=[('kneighborsclassifier', KNeighborsClassifier())]),
'LogisticRegression': Pipeline(steps=[('logisticregression', LogisticRegression())]),
'RandomForestClassifier': Pipeline(steps=[('randomforestclassifier', RandomForestClassifier())])}
# from sklearn.model_selection import RandomizedSearchCV
# random_search = RandomizedSearchCV(estimator=model, param_distributions=params, n_jobs=-1)
# Perform random search for each algorithm
best_params = {}
for algo, clf in classifiers.items():
search = RandomizedSearchCV(clf, param_distributions[algo], n_iter=10, cv=3, random_state=42)
search.fit(X_selected_400, y_selected_400)
best_params[algo] = search.best_params_
# Print best parameters for each algorithm
for algo, params in best_params.items():
print(f"Best parameters for {algo}: {params}")
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 3448 epochs took 2584 seconds
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 43.1min remaining: 0.0s [Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 43.1min finished [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 4136 epochs took 2975 seconds
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 49.6min remaining: 0.0s [Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 49.6min finished [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 3491 epochs took 2550 seconds
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 42.5min remaining: 0.0s [Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 42.5min finished [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 467 epochs took 246 seconds
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 4.1min remaining: 0.0s [Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 4.1min finished [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 445 epochs took 231 seconds
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 3.9min remaining: 0.0s [Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 3.9min finished [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 556 epochs took 293 seconds
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 4.9min remaining: 0.0s [Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 4.9min finished [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 1290 epochs took 839 seconds
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 14.0min remaining: 0.0s [Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 14.0min finished [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 376 epochs took 245 seconds
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 4.1min remaining: 0.0s [Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 4.1min finished [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 801 epochs took 592 seconds
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 9.9min remaining: 0.0s [Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 9.9min finished [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 1945 epochs took 1221 seconds
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 20.3min remaining: 0.0s [Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 20.3min finished [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 1236 epochs took 778 seconds
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 13.0min remaining: 0.0s [Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 13.0min finished [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 1969 epochs took 1266 seconds
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 21.1min remaining: 0.0s [Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 21.1min finished
[LibLinear][LibLinear][LibLinear]
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 1179 epochs took 781 seconds
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 13.0min finished [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 659 epochs took 437 seconds
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 7.3min finished [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 1126 epochs took 747 seconds
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 12.4min finished [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 3466 epochs took 2802 seconds
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 46.7min remaining: 0.0s [Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 46.7min finished [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 4379 epochs took 3672 seconds
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 61.2min remaining: 0.0s [Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 61.2min finished [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 3770 epochs took 3503 seconds
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 58.4min remaining: 0.0s [Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 58.4min finished [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 468 epochs took 313 seconds
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 5.2min remaining: 0.0s [Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 5.2min finished [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 444 epochs took 295 seconds
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 4.9min remaining: 0.0s [Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 4.9min finished [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 555 epochs took 367 seconds
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 6.1min remaining: 0.0s [Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 6.1min finished
[LibLinear]Best parameters for GradientBoostingClassifier: {'gradientboostingclassifier__n_estimators': 120, 'gradientboostingclassifier__learning_rate': 0.3}
Best parameters for KNN_CLF: {'kneighborsclassifier__weights': 'distance', 'kneighborsclassifier__n_neighbors': 6}
Best parameters for LogisticRegression: {'logisticregression__verbose': 3, 'logisticregression__solver': 'liblinear', 'logisticregression__penalty': 'l2', 'logisticregression__max_iter': 5000, 'logisticregression__C': 2.0}
Best parameters for RandomForestClassifier: {'randomforestclassifier__n_estimators': 50, 'randomforestclassifier__min_samples_split': 5, 'randomforestclassifier__min_samples_leaf': 1, 'randomforestclassifier__max_features': 'sqrt', 'randomforestclassifier__max_depth': 100, 'randomforestclassifier__bootstrap': True}
Best Parameters
:
:
:
df_X_train_transformed_resampled
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | 14643 | 14644 | 14645 | 14646 | 14647 | 14648 | 14649 | 14650 | 14651 | 14652 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -1.226519 | 0.101380 | 1.140316 | 0.207758 | -0.310264 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.298734 | 0.124051 | 0.281013 | 0.296203 |
| 1 | -1.226519 | 1.220209 | -0.876950 | 2.036438 | -0.310264 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.471074 | 0.041322 | 0.247934 | 0.239669 |
| 2 | 0.815316 | -0.559747 | -0.876950 | 0.207758 | -0.310264 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.351990 | 0.078358 | 0.268657 | 0.300995 |
| 3 | 0.815316 | -0.559747 | -0.876950 | 0.207758 | 1.569830 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.438144 | 0.051546 | 0.280928 | 0.229381 |
| 4 | -1.226519 | -0.102044 | 1.140316 | 2.036438 | -0.310264 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.478992 | 0.033613 | 0.142857 | 0.344538 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 7797 | -1.226519 | -0.152900 | -0.876950 | 0.207758 | -0.310264 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.079652 | 0.080377 | 0.495293 | 0.344678 |
| 7798 | 0.815316 | 3.559580 | 1.140316 | 2.036438 | 3.449925 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.365782 | 0.103245 | 0.300885 | 0.230089 |
| 7799 | -1.226519 | -0.508891 | 1.140316 | -1.620921 | -0.310264 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.323767 | 0.060987 | 0.309417 | 0.305830 |
| 7800 | -1.226519 | -0.458035 | -0.876950 | 0.207758 | -0.310264 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.393939 | 0.145455 | 0.230303 | 0.230303 |
| 7801 | -1.226519 | -0.508891 | 1.140316 | -1.620921 | -0.310264 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.323767 | 0.060987 | 0.309417 | 0.305830 |
7802 rows × 14653 columns
df_val_X_val_transformed
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | 14643 | 14644 | 14645 | 14646 | 14647 | 14648 | 14649 | 14650 | 14651 | 14652 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -1.226519 | -0.559747 | 1.140316 | -1.620921 | -0.310264 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.285714 | 0.069426 | 0.353805 | 0.291055 |
| 1 | 0.815316 | -0.051188 | -0.876950 | 0.207758 | 2.196528 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.177449 | 0.052372 | 0.411583 | 0.358595 |
| 2 | 0.815316 | -0.458035 | -0.876950 | 0.207758 | -0.310264 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.457912 | 0.097643 | 0.249158 | 0.195286 |
| 3 | 0.815316 | -0.508891 | -0.876950 | 0.207758 | -0.310264 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.141079 | 0.099585 | 0.435685 | 0.323651 |
| 4 | -1.226519 | -0.508891 | -0.876950 | -1.620921 | -0.184925 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.551227 | 0.038961 | 0.284271 | 0.125541 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1795 | 0.815316 | -0.458035 | 1.140316 | 0.207758 | 2.823227 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.246454 | 0.129433 | 0.352837 | 0.271277 |
| 1796 | 0.815316 | -0.203756 | 1.140316 | -1.620921 | 2.196528 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.203554 | 0.061389 | 0.518578 | 0.216478 |
| 1797 | 0.815316 | -0.508891 | -0.876950 | 0.207758 | -0.310264 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.479310 | 0.068966 | 0.272414 | 0.179310 |
| 1798 | -1.226519 | -0.458035 | 1.140316 | 0.207758 | -0.310264 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.286344 | 0.193833 | 0.330396 | 0.189427 |
| 1799 | -1.226519 | -0.508891 | 1.140316 | -1.620921 | -0.310264 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.286822 | 0.077519 | 0.275194 | 0.360465 |
1800 rows × 14653 columns
# classifier_lr = LogisticRegression(verbose= 1,penalty = 'l2',max_iter= 1000, C = 3.0)
classifier_lr = LogisticRegression(verbose= 3,solver = 'liblinear', penalty = 'l2',max_iter= 5000, C = 2.0)
pipeline_final_lr = Pipeline(steps=[
# ('pipe_my',my_sklearn_pipeline),
('LogisticRegression_classifier',classifier_lr)
],
memory = 'tmp/cache'
)
pipeline_final_lr
Pipeline(memory='tmp/cache',
steps=[('LogisticRegression_classifier',
LogisticRegression(C=2.0, max_iter=5000, solver='liblinear',
verbose=3))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(memory='tmp/cache',
steps=[('LogisticRegression_classifier',
LogisticRegression(C=2.0, max_iter=5000, solver='liblinear',
verbose=3))])LogisticRegression(C=2.0, max_iter=5000, solver='liblinear', verbose=3)
pipeline_final_lr.fit(df_X_train_transformed_resampled,y_train_resampled)
[LibLinear]
Pipeline(memory='tmp/cache',
steps=[('LogisticRegression_classifier',
LogisticRegression(C=2.0, max_iter=5000, solver='liblinear',
verbose=3))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(memory='tmp/cache',
steps=[('LogisticRegression_classifier',
LogisticRegression(C=2.0, max_iter=5000, solver='liblinear',
verbose=3))])LogisticRegression(C=2.0, max_iter=5000, solver='liblinear', verbose=3)
y_pred_lr=pipeline_final_lr.predict(df_val_X_val_transformed)
from sklearn.metrics import precision_score
precision_score(y_val, y_pred_lr, average= 'weighted')
0.37048772873553426
from sklearn.metrics import cohen_kappa_score
kp_lr=cohen_kappa_score(y_val,y_pred_lr)
kp_lr
0.17754110260107603
classifier_rf = RandomForestClassifier(n_estimators= 50, max_depth= 50, bootstrap= True)
# model_rf = make_pipeline(my_sklearn_pipeline, classifier_rf)
pipeline_final_rf = Pipeline(steps=[
# ('pipe_my',my_sklearn_pipeline),
('RandomForest_Classifier',classifier_rf)
],
memory = 'tmp/cache'
)
pipeline_final_rf.fit(df_X_train_transformed_resampled,y_train_resampled)
Pipeline(memory='tmp/cache',
steps=[('RandomForest_Classifier',
RandomForestClassifier(max_depth=50, n_estimators=50))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(memory='tmp/cache',
steps=[('RandomForest_Classifier',
RandomForestClassifier(max_depth=50, n_estimators=50))])RandomForestClassifier(max_depth=50, n_estimators=50)
y_pred_rf=pipeline_final_rf.predict(df_val_X_val_transformed)
from sklearn.metrics import precision_score
precision_score(y_val, y_pred_rf, average= 'weighted')
0.4094699812714273
from sklearn.metrics import cohen_kappa_score
kp_rf=cohen_kappa_score(y_val,y_pred_rf)
kp_rf
0.19771296367318925
classifier_gb = GradientBoostingClassifier(n_estimators= 120, learning_rate = 0.2)
# model_gb = make_pipeline(my_sklearn_pipeline, classifier_gb)
pipeline_final_gb = Pipeline(steps=[
# ('pipe_my',my_sklearn_pipeline),
('GradientBoosting_Classifier',classifier_gb)
],
memory = 'tmp/cache'
)
pipeline_final_gb
Pipeline(memory='tmp/cache',
steps=[('GradientBoosting_Classifier',
GradientBoostingClassifier(learning_rate=0.2,
n_estimators=120))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(memory='tmp/cache',
steps=[('GradientBoosting_Classifier',
GradientBoostingClassifier(learning_rate=0.2,
n_estimators=120))])GradientBoostingClassifier(learning_rate=0.2, n_estimators=120)
pipeline_final_gb.fit(df_X_train_transformed_resampled,y_train_resampled)
Pipeline(memory='tmp/cache',
steps=[('GradientBoosting_Classifier',
GradientBoostingClassifier(learning_rate=0.2,
n_estimators=120))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(memory='tmp/cache',
steps=[('GradientBoosting_Classifier',
GradientBoostingClassifier(learning_rate=0.2,
n_estimators=120))])GradientBoostingClassifier(learning_rate=0.2, n_estimators=120)
y_pred_gb=pipeline_final_gb.predict(df_val_X_val_transformed)
from sklearn.metrics import precision_score
precision_score(y_val, y_pred_gb, average= 'weighted')
0.3814493836551052
from sklearn.metrics import cohen_kappa_score
kp_gb=cohen_kappa_score(y_val,y_pred_gb)
kp_gb
0.18104967629188817
classifier_knn = KNeighborsClassifier(n_neighbors= 4)
pipeline_final_knn = Pipeline(steps=[
# ('pipe_my',my_sklearn_pipeline),
('KNeighbors_Classifier',classifier_knn)
],
memory = 'tmp/cache'
)
pipeline_final_knn
Pipeline(memory='tmp/cache',
steps=[('KNeighbors_Classifier', KNeighborsClassifier(n_neighbors=4))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(memory='tmp/cache',
steps=[('KNeighbors_Classifier', KNeighborsClassifier(n_neighbors=4))])KNeighborsClassifier(n_neighbors=4)
pipeline_final_knn.fit(df_X_train_transformed_resampled,y_train_resampled)
Pipeline(memory='tmp/cache',
steps=[('KNeighbors_Classifier', KNeighborsClassifier(n_neighbors=4))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(memory='tmp/cache',
steps=[('KNeighbors_Classifier', KNeighborsClassifier(n_neighbors=4))])KNeighborsClassifier(n_neighbors=4)
y_pred_knn=pipeline_final_knn.predict(df_val_X_val_transformed)
from sklearn.metrics import precision_score
precision_score(y_val, y_pred_knn, average= 'weighted')
0.3370042872206625
from sklearn.metrics import cohen_kappa_score
kp_knn=cohen_kappa_score(y_val,y_pred_knn)
kp_knn
0.12328131474431225
df_test_copy.head(1)
| Type | Age | Gender | Color1 | Color2 | Color3 | MaturitySize | FurLength | Vaccinated | Dewormed | Sterilized | Health | Fee | Description | Images | Breed | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Cat | 1.0 | Male | Black | White | Unknown | Small | Yes | No | No | No | Healthy | 0.0 | kitten for adoption, pls call for enquiry, off... | C:\Users\praba\Documents\GitHub\UCA SEMESTER 2... | Domestic_Short_Hair |
df_test_copy["Images"][0]
'C:\\Users\\praba\\Documents\\GitHub\\UCA SEMESTER 2 M1\\deeplearning 2\\final project and lab\\drive-download-20240301T144626Z-001\\test_images_all\\5df99d229-2.jpg'
df_test_copy_transformed = my_sklearn_pipeline.transform(df_test_copy)
import pandas as pd
dense_array_test = df_test_copy_transformed.toarray()
df_test_copy_transformed_dataframe = pd.DataFrame(dense_array_test)
df_test_copy_transformed_dataframe.head()
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | 14643 | 14644 | 14645 | 14646 | 14647 | 14648 | 14649 | 14650 | 14651 | 14652 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -1.226519 | -0.559747 | 1.140316 | -1.620921 | -0.310264 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.261236 | 0.070225 | 0.370787 | 0.297753 |
| 1 | 0.815316 | -0.203756 | 1.140316 | 0.207758 | -0.310264 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.337393 | 0.076736 | 0.331303 | 0.254568 |
| 2 | 0.815316 | -0.508891 | -0.876950 | 0.207758 | -0.310264 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.503165 | 0.060127 | 0.164557 | 0.272152 |
| 3 | 0.815316 | -0.458035 | -0.876950 | 0.207758 | -0.310264 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.314961 | 0.031496 | 0.244094 | 0.409449 |
| 4 | -1.226519 | -0.458035 | -0.876950 | 0.207758 | -0.184925 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.440789 | 0.019737 | 0.210526 | 0.328947 |
5 rows × 14653 columns
# df_test_copy_transformed_dataframe.to_csv('df_test_copy_transformed_dataframe_df.csv')
# df_test_copy_transformed_dataframe = pd.read_csv('df_test_copy_transformed_dataframe_df.csv')
y_pred_random_true = pipeline_final_rf.predict(df_test_copy_transformed_dataframe)
y_pred_logistic_true = pipeline_final_lr.predict(df_test_copy_transformed_dataframe)
y_pred_knn_true = pipeline_final_knn.predict(df_test_copy_transformed_dataframe)
y_pred_gbc_true = pipeline_final_gb.predict(df_test_copy_transformed_dataframe)
models_pred = {}
models_pred['logistic'] = y_pred_logistic_true
models_pred['random'] = y_pred_random_true
models_pred['knn'] = y_pred_knn_true
# models_pred['gbc'] = y_pred_gbc_true
df_final_result_test_all = pd.DataFrame(models_pred)
print(df_final_result_test_all)
logistic random knn 0 3.0 2.0 1.0 1 2.0 2.0 3.0 2 3.0 2.0 3.0 3 4.0 4.0 4.0 4 3.0 2.0 2.0 .. ... ... ... 495 4.0 2.0 4.0 496 1.0 1.0 1.0 497 4.0 4.0 4.0 498 4.0 4.0 4.0 499 4.0 2.0 0.0 [500 rows x 3 columns]
df_final_result_test_all.to_csv('df_final_result_test_all.csv')
At the end of the project, you must submit:
an executer notebook writed like a report in order to explain your choice and your strategy your prediction for the test part (2 columns csv with id and prediction rate) the evaluation metric is the quadratic kappa score (available in sklearn)
df_y_pred_random_true = pd.DataFrame(y_pred_random_true,columns=['prediction rate'])
print(df_y_pred_random_true)
prediction rate 0 2.0 1 2.0 2 2.0 3 4.0 4 2.0 .. ... 495 2.0 496 1.0 497 4.0 498 4.0 499 2.0 [500 rows x 1 columns]
# df_y_pred_random_true.to_csv(' result_ml.csv')
df_y_pred_random_true.to_csv(' result_ml.csv',index_label = 'id')
# df_final_result_test_all['logistic'].to_csv(' result.csv', index=False)
import pandas as pd
dense_array_X_train_transformed_resampled = X_train_transformed_resampled.toarray()
df_X_train_transformed_resampled = pd.DataFrame(dense_array)
df_X_train_transformed_resampled.head(2)
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | 14643 | 14644 | 14645 | 14646 | 14647 | 14648 | 14649 | 14650 | 14651 | 14652 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -1.226519 | 0.101380 | 1.140316 | 0.207758 | -0.310264 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.298734 | 0.124051 | 0.281013 | 0.296203 |
| 1 | -1.226519 | 1.220209 | -0.876950 | 2.036438 | -0.310264 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.471074 | 0.041322 | 0.247934 | 0.239669 |
2 rows × 14653 columns
import pandas as pd
dense_array_val_X_val_transformed = X_val_transformed.toarray()
df_val_X_val_transformed = pd.DataFrame(dense_array_val_X_val_transformed)
df_val_X_val_transformed.head(2)
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | 14643 | 14644 | 14645 | 14646 | 14647 | 14648 | 14649 | 14650 | 14651 | 14652 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -1.226519 | -0.559747 | 1.140316 | -1.620921 | -0.310264 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.285714 | 0.069426 | 0.353805 | 0.291055 |
| 1 | 0.815316 | -0.051188 | -0.876950 | 0.207758 | 2.196528 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.177449 | 0.052372 | 0.411583 | 0.358595 |
2 rows × 14653 columns
dense_array_X_train_transformed_resampled.shape
(7802, 14653)
dense_array_val_X_val_transformed.shape
(1800, 14653)
#Dependencies
import keras
from keras.models import Sequential
from keras.layers import Dense
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Input, Dense, Dropout
model_dl2 = Sequential([
Input(shape=(dense_array_X_train_transformed_resampled.shape[1],)),
Dense(1024, activation='relu'),
Dropout(0.3),
Dense(512, activation='relu'),
Dropout(0.3),
Dense(256, activation='relu'),
Dropout(0.3),
Dense(128, activation='relu'),
Dropout(0.3),
Dense(64, activation='relu'),
Dropout(0.3),
Dense(32, activation='relu'),
Dense(5, activation='softmax')
])
# from tensorflow.keras.utils import plot_model
# from tensorflow.keras.utils import plot_model
from keras.utils import plot_model
plot_model(model_dl2, to_file='model_plot2.png',
show_shapes=True,
show_layer_names=True,
layer_range=None,
show_layer_activations=True,
show_trainable=True)
model_dl2.summary()
Model: "sequential_2"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ dense_14 (Dense) │ (None, 1024) │ 15,005,696 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_10 (Dropout) │ (None, 1024) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_15 (Dense) │ (None, 512) │ 524,800 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_11 (Dropout) │ (None, 512) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_16 (Dense) │ (None, 256) │ 131,328 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_12 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_17 (Dense) │ (None, 128) │ 32,896 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_13 (Dropout) │ (None, 128) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_18 (Dense) │ (None, 64) │ 8,256 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_14 (Dropout) │ (None, 64) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_19 (Dense) │ (None, 32) │ 2,080 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_20 (Dense) │ (None, 5) │ 165 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 15,705,221 (59.91 MB)
Trainable params: 15,705,221 (59.91 MB)
Non-trainable params: 0 (0.00 B)
model_dl2.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
from tensorflow.keras.callbacks import EarlyStopping
early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)
# Train the model with early stopping
history_model_dl2 = model_dl2.fit(dense_array_X_train_transformed_resampled, y_train_resampled, epochs=50, validation_split=0.2, batch_size=32, callbacks=[early_stopping])
Epoch 1/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 34s 148ms/step - accuracy: 0.2741 - loss: 1.5078 - val_accuracy: 0.2044 - val_loss: 2.0752 Epoch 2/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 30s 155ms/step - accuracy: 0.4151 - loss: 1.3329 - val_accuracy: 0.2261 - val_loss: 1.9477 Epoch 3/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 30s 153ms/step - accuracy: 0.5316 - loss: 1.1113 - val_accuracy: 0.2389 - val_loss: 1.8150 Epoch 4/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 28s 144ms/step - accuracy: 0.6922 - loss: 0.7795 - val_accuracy: 0.4305 - val_loss: 1.6681 Epoch 5/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 28s 145ms/step - accuracy: 0.7898 - loss: 0.5614 - val_accuracy: 0.5208 - val_loss: 1.6075 Epoch 6/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 29s 146ms/step - accuracy: 0.8702 - loss: 0.3818 - val_accuracy: 0.5349 - val_loss: 1.8078 Epoch 7/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 28s 144ms/step - accuracy: 0.9043 - loss: 0.3143 - val_accuracy: 0.5279 - val_loss: 1.9766 Epoch 8/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 28s 143ms/step - accuracy: 0.9204 - loss: 0.2285 - val_accuracy: 0.5490 - val_loss: 2.3494 Epoch 9/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 32s 161ms/step - accuracy: 0.9337 - loss: 0.1956 - val_accuracy: 0.5400 - val_loss: 2.2032 Epoch 10/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 30s 152ms/step - accuracy: 0.9395 - loss: 0.1688 - val_accuracy: 0.5375 - val_loss: 2.5356
import matplotlib.pyplot as plt
plt.figure(figsize=(20, 6))
# Plot the first subplot (loss)
plt.subplot(1, 2, 1) # 1 row, 2 columns, first subplot
plt.plot(history_model_dl2.history["loss"])
plt.plot(history_model_dl2.history["val_loss"])
plt.title("Validation Loss & Training Loss")
plt.xlabel("Epochs")
plt.ylabel("Loss")
leg = plt.legend(["Training Loss", "Validation Loss"], loc="upper right")
# Plot the second subplot (accuracy)
plt.subplot(1, 2, 2) # 1 row, 2 columns, second subplot
plt.plot(history_model_dl2.history["accuracy"])
plt.plot(history_model_dl2.history["val_accuracy"])
plt.title("Validation Accuracy & Training Accuracy")
plt.xlabel("Epochs")
plt.ylabel("Accuracy")
leg = plt.legend(["Training Accuracy", "Validation Accuracy"], loc="lower right")
plt.show()
So ths deep learning model is overfitted
y_pred_val_model_dl2 = model_dl2.predict(dense_array_val_X_val_transformed)
57/57 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step
predicted_class_model_dl2 = np.argmax(y_pred_val_model_dl2, axis=1)
predicted_class_model_dl2
array([1, 3, 2, ..., 2, 3, 1], dtype=int64)
from sklearn.metrics import cohen_kappa_score
kp_model_dl2=cohen_kappa_score(y_val,predicted_class_model_dl2)
kp_model_dl2
0.16717386770164133
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Input, Dense, Dropout, BatchNormalization
from tensorflow.keras import regularizers
model_dl3 = Sequential([
Input(shape=(dense_array_X_train_transformed_resampled.shape[1],)),
Dense(1024, activation='relu', kernel_regularizer=regularizers.l2(0.003)),
BatchNormalization(),
Dropout(0.3),
Dense(512, activation='relu', kernel_regularizer=regularizers.l2(0.003)),
BatchNormalization(),
Dropout(0.3),
Dense(256, activation='relu', kernel_regularizer=regularizers.l2(0.003)),
BatchNormalization(),
Dropout(0.3),
Dense(128, activation='relu', kernel_regularizer=regularizers.l2(0.003)),
BatchNormalization(),
Dropout(0.3),
Dense(64, activation='relu', kernel_regularizer=regularizers.l2(0.003)),
BatchNormalization(),
Dropout(0.3),
Dense(32, activation='relu', kernel_regularizer=regularizers.l2(0.003)),
BatchNormalization(),
Dense(5, activation='softmax')
])
# from tensorflow.keras.utils import plot_model
# plot_model(model3, to_file='model_plot4.png', show_shapes=True, show_laye
# from tensorflow.keras.utils import plot_model
from keras.utils import plot_model
# plot_model(model3, to_file='model_plot4.png', show_shapes=True, show_laye
plot_model(model_dl3, to_file='model_plot3.png',
show_shapes=True,
show_layer_names=True,
layer_range=None,
show_layer_activations=True,
show_trainable=True)
model_dl3.summary()
Model: "sequential_6"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ dense_22 (Dense) │ (None, 1024) │ 15,005,696 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ batch_normalization_12 │ (None, 1024) │ 4,096 │ │ (BatchNormalization) │ │ │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_23 (Dropout) │ (None, 1024) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_23 (Dense) │ (None, 512) │ 524,800 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ batch_normalization_13 │ (None, 512) │ 2,048 │ │ (BatchNormalization) │ │ │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_24 (Dropout) │ (None, 512) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_24 (Dense) │ (None, 256) │ 131,328 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ batch_normalization_14 │ (None, 256) │ 1,024 │ │ (BatchNormalization) │ │ │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_25 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_25 (Dense) │ (None, 128) │ 32,896 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ batch_normalization_15 │ (None, 128) │ 512 │ │ (BatchNormalization) │ │ │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_26 (Dropout) │ (None, 128) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_26 (Dense) │ (None, 64) │ 8,256 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ batch_normalization_16 │ (None, 64) │ 256 │ │ (BatchNormalization) │ │ │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_27 (Dropout) │ (None, 64) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_27 (Dense) │ (None, 32) │ 2,080 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ batch_normalization_17 │ (None, 32) │ 128 │ │ (BatchNormalization) │ │ │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_28 (Dense) │ (None, 5) │ 165 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 15,713,285 (59.94 MB)
Trainable params: 15,709,253 (59.93 MB)
Non-trainable params: 4,032 (15.75 KB)
model_dl3.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
from tensorflow.keras.callbacks import EarlyStopping
early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)
history_model_dl3 = model_dl3.fit(dense_array_X_train_transformed_resampled, y_train_resampled, epochs=50, validation_split=0.2, batch_size=32, callbacks=[early_stopping])
Epoch 1/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 46s 204ms/step - accuracy: 0.2359 - loss: 7.2854 - val_accuracy: 0.1749 - val_loss: 7.3374 Epoch 2/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 37s 191ms/step - accuracy: 0.3260 - loss: 6.8489 - val_accuracy: 0.2159 - val_loss: 6.6761 Epoch 3/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 38s 192ms/step - accuracy: 0.3846 - loss: 5.8009 - val_accuracy: 0.2242 - val_loss: 5.7876 Epoch 4/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 41s 209ms/step - accuracy: 0.4397 - loss: 4.7753 - val_accuracy: 0.2249 - val_loss: 5.0507 Epoch 5/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 42s 214ms/step - accuracy: 0.4891 - loss: 4.0082 - val_accuracy: 0.2268 - val_loss: 4.6268 Epoch 6/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 41s 210ms/step - accuracy: 0.5415 - loss: 3.4224 - val_accuracy: 0.2338 - val_loss: 4.1884 Epoch 7/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 42s 213ms/step - accuracy: 0.5832 - loss: 3.0524 - val_accuracy: 0.2569 - val_loss: 3.8318 Epoch 8/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 41s 210ms/step - accuracy: 0.6217 - loss: 2.7813 - val_accuracy: 0.2774 - val_loss: 3.7865 Epoch 9/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 42s 216ms/step - accuracy: 0.6509 - loss: 2.7133 - val_accuracy: 0.3318 - val_loss: 3.6195 Epoch 10/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 40s 203ms/step - accuracy: 0.6718 - loss: 2.6204 - val_accuracy: 0.3261 - val_loss: 3.7071 Epoch 11/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 39s 198ms/step - accuracy: 0.6846 - loss: 2.5948 - val_accuracy: 0.3927 - val_loss: 3.5583 Epoch 12/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 41s 208ms/step - accuracy: 0.6977 - loss: 2.5771 - val_accuracy: 0.4183 - val_loss: 3.4240 Epoch 13/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 42s 214ms/step - accuracy: 0.7212 - loss: 2.5433 - val_accuracy: 0.4273 - val_loss: 3.4747 Epoch 14/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 44s 226ms/step - accuracy: 0.7304 - loss: 2.5124 - val_accuracy: 0.3299 - val_loss: 3.8769 Epoch 15/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 42s 212ms/step - accuracy: 0.7457 - loss: 2.5853 - val_accuracy: 0.4254 - val_loss: 3.6232 Epoch 16/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 43s 217ms/step - accuracy: 0.7475 - loss: 2.5582 - val_accuracy: 0.5010 - val_loss: 3.4119 Epoch 17/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 41s 211ms/step - accuracy: 0.7672 - loss: 2.4889 - val_accuracy: 0.4600 - val_loss: 3.4950 Epoch 18/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 40s 203ms/step - accuracy: 0.7680 - loss: 2.4787 - val_accuracy: 0.4202 - val_loss: 3.6264 Epoch 19/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 37s 187ms/step - accuracy: 0.7662 - loss: 2.5353 - val_accuracy: 0.4222 - val_loss: 3.7756 Epoch 20/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 37s 188ms/step - accuracy: 0.7672 - loss: 2.5306 - val_accuracy: 0.4837 - val_loss: 3.5081 Epoch 21/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 38s 193ms/step - accuracy: 0.7785 - loss: 2.4854 - val_accuracy: 0.4856 - val_loss: 3.4491
import matplotlib.pyplot as plt
plt.figure(figsize=(20, 6))
# Plot the first subplot (loss)
plt.subplot(1, 2, 1) # 1 row, 2 columns, first subplot
plt.plot(history_model_dl3.history["loss"])
plt.plot(history_model_dl3.history["val_loss"])
plt.title("Validation Loss & Training Loss")
plt.xlabel("Epochs")
plt.ylabel("Loss")
leg = plt.legend(["Training Loss", "Validation Loss"], loc="upper right")
# Plot the second subplot (accuracy)
plt.subplot(1, 2, 2) # 1 row, 2 columns, second subplot
plt.plot(history_model_dl3.history["accuracy"])
plt.plot(history_model_dl3.history["val_accuracy"])
plt.title("Validation Accuracy & Training Accuracy")
plt.xlabel("Epochs")
plt.ylabel("Accuracy")
leg = plt.legend(["Training Accuracy", "Validation Accuracy"], loc="lower right")
plt.show()
y_pred_val_model_dl3 = model_dl3.predict(dense_array_val_X_val_transformed)
57/57 ━━━━━━━━━━━━━━━━━━━━ 1s 15ms/step
predicted_class_model_dl3 = np.argmax(y_pred_val_model_dl3, axis=1)
predicted_class_model_dl3
array([1, 4, 2, ..., 3, 2, 0], dtype=int64)
from sklearn.metrics import cohen_kappa_score
kp_model_dl3=cohen_kappa_score(y_val,predicted_class_model_dl3)
kp_model_dl3
0.20020809195432487
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Bidirectional, LSTM, Dense, Dropout
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
dense_array_X_train_transformed_resampled_reshaped = dense_array_X_train_transformed_resampled.reshape(dense_array_X_train_transformed_resampled.shape[0], 1, dense_array_X_train_transformed_resampled.shape[1])
# Build the Bidirectional LSTM model
model_lstm = Sequential([
Bidirectional(LSTM(64, return_sequences=True), input_shape=(dense_array_X_train_transformed_resampled_reshaped.shape[1], dense_array_val_X_val_transformed_reshaped.shape[2])),
Dropout(0.5),
Bidirectional(LSTM(32)),
Dropout(0.5),
Dense(64, activation='relu'),
Dropout(0.5),
Dense(5, activation='softmax')
])
# from tensorflow.keras.utils import plot_model
# plot_model(model3, to_file='model_plot4.png', show_shapes=True, show_laye
# from tensorflow.keras.utils import plot_model
from keras.utils import plot_model
# plot_model(model3, to_file='model_plot4.png', show_shapes=True, show_laye
plot_model(model_lstm, to_file='model_plot_lstm.png',
show_shapes=True,
show_layer_names=True,
layer_range=None,
show_layer_activations=True,
show_trainable=True)
model_lstm.summary()
Model: "sequential_3"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ bidirectional_4 (Bidirectional) │ (None, 1, 128) │ 7,535,616 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_12 (Dropout) │ (None, 1, 128) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ bidirectional_5 (Bidirectional) │ (None, 64) │ 41,216 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_13 (Dropout) │ (None, 64) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_11 (Dense) │ (None, 64) │ 4,160 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_14 (Dropout) │ (None, 64) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_12 (Dense) │ (None, 5) │ 325 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 7,581,317 (28.92 MB)
Trainable params: 7,581,317 (28.92 MB)
Non-trainable params: 0 (0.00 B)
# Compile the model
model_lstm.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
#
# dense_array_val_X_val_transformed_reshaped.shape
from tensorflow.keras.callbacks import EarlyStopping
early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)
# Train the model with early stopping
# history_model_dl3 = model_dl3.fit(df, y_train_resampled, epochs=50, validation_split=0.2, batch_size=32, callbacks=[early_stopping])
history_model_lstm = model_lstm.fit(dense_array_X_train_transformed_resampled_reshaped, y_train_resampled, epochs=50, validation_split=0.2, batch_size=32, callbacks=[early_stopping])
Epoch 1/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 24s 88ms/step - accuracy: 0.2715 - loss: 1.5333 - val_accuracy: 0.2012 - val_loss: 2.0395 Epoch 2/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 16s 80ms/step - accuracy: 0.3717 - loss: 1.3764 - val_accuracy: 0.2422 - val_loss: 1.8430 Epoch 3/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 16s 82ms/step - accuracy: 0.5013 - loss: 1.1929 - val_accuracy: 0.2639 - val_loss: 1.7744 Epoch 4/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 17s 84ms/step - accuracy: 0.6173 - loss: 0.9766 - val_accuracy: 0.3530 - val_loss: 1.7644 Epoch 5/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 16s 82ms/step - accuracy: 0.7000 - loss: 0.7893 - val_accuracy: 0.4158 - val_loss: 1.8243 Epoch 6/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 15s 78ms/step - accuracy: 0.7588 - loss: 0.6479 - val_accuracy: 0.4869 - val_loss: 1.8464 Epoch 7/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 15s 78ms/step - accuracy: 0.8180 - loss: 0.5200 - val_accuracy: 0.4990 - val_loss: 2.0458 Epoch 8/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 15s 78ms/step - accuracy: 0.8430 - loss: 0.4533 - val_accuracy: 0.5106 - val_loss: 2.2172 Epoch 9/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 16s 79ms/step - accuracy: 0.8464 - loss: 0.4223 - val_accuracy: 0.5208 - val_loss: 2.1710
import matplotlib.pyplot as plt
plt.figure(figsize=(20, 6))
# Plot the first subplot (loss)
plt.subplot(1, 2, 1) # 1 row, 2 columns, first subplot
plt.plot(history_model_lstm.history["loss"])
plt.plot(history_model_lstm.history["val_loss"])
plt.title("Validation Loss & Training Loss")
plt.xlabel("Epochs")
plt.ylabel("Loss")
leg = plt.legend(["Training Loss", "Validation Loss"], loc="upper right")
# Plot the second subplot (accuracy)
plt.subplot(1, 2, 2) # 1 row, 2 columns, second subplot
plt.plot(history_model_lstm.history["accuracy"])
plt.plot(history_model_lstm.history["val_accuracy"])
plt.title("Validation Accuracy & Training Accuracy")
plt.xlabel("Epochs")
plt.ylabel("Accuracy")
leg = plt.legend(["Training Accuracy", "Validation Accuracy"], loc="lower right")
plt.show()
So ths deep learning model is overfitted
dense_array_val_X_val_transformed_reshaped = dense_array_val_X_val_transformed.reshape(dense_array_val_X_val_transformed.shape[0], 1, dense_array_val_X_val_transformed.shape[1])
y_pred_val_model_lstm = model_lstm.predict(dense_array_val_X_val_transformed_reshaped)
57/57 ━━━━━━━━━━━━━━━━━━━━ 2s 17ms/step
predicted_class_model_lstm = np.argmax(y_pred_val_model_lstm, axis=1)
predicted_class_model_lstm
array([1, 2, 2, ..., 3, 2, 1], dtype=int64)
KAPPA SCORE
from sklearn.metrics import cohen_kappa_score
kp_model_lstm=cohen_kappa_score(y_val,predicted_class_model_lstm)
kp_model_lstm
0.1836346383642512
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
# dense_array_X_train_transformed_resampled_reshaped = dense_array_X_train_transformed_resampled.reshape(dense_array_X_train_transformed_resampled.shape[0], 1, dense_array_X_train_transformed_resampled.shape[1])
# Build the LSTM model
model_lstm_normal = Sequential([
LSTM(64, return_sequences=True, input_shape=(dense_array_X_train_transformed_resampled_reshaped.shape[1], dense_array_X_train_transformed_resampled_reshaped.shape[2])),
Dropout(0.5),
LSTM(32),
Dropout(0.5),
Dense(64, activation='relu'),
Dropout(0.5),
Dense(5, activation='softmax')
])
# from tensorflow.keras.utils import plot_model
# plot_model(model3, to_file='model_plot4.png', show_shapes=True, show_laye
# from tensorflow.keras.utils import plot_model
from keras.utils import plot_model
# plot_model(model3, to_file='model_plot4.png', show_shapes=True, show_laye
plot_model(model_lstm_normal, to_file='model_plot_lstm_normal.png',
show_shapes=True,
show_layer_names=True,
layer_range=None,
show_layer_activations=True,
show_trainable=True)
model_lstm_normal.summary()
Model: "sequential_4"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_6 (LSTM) │ (None, 1, 64) │ 3,767,808 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_15 (Dropout) │ (None, 1, 64) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_7 (LSTM) │ (None, 32) │ 12,416 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_16 (Dropout) │ (None, 32) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_13 (Dense) │ (None, 64) │ 2,112 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_17 (Dropout) │ (None, 64) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_14 (Dense) │ (None, 5) │ 325 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 3,782,661 (14.43 MB)
Trainable params: 3,782,661 (14.43 MB)
Non-trainable params: 0 (0.00 B)
# Compile the model
model_lstm_normal.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
#
from tensorflow.keras.callbacks import EarlyStopping
early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)
# Train the model with early stopping
# history_model_dl3 = model_dl3.fit(df, y_train_resampled, epochs=50, validation_split=0.2, batch_size=32, callbacks=[early_stopping])
history_model_lstm = model_lstm_normal.fit(dense_array_X_train_transformed_resampled_reshaped, y_train_resampled, epochs=50, validation_split=0.2, batch_size=32, callbacks=[early_stopping])
Epoch 1/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 14s 47ms/step - accuracy: 0.2498 - loss: 1.5455 - val_accuracy: 0.2037 - val_loss: 2.0815 Epoch 2/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 9s 43ms/step - accuracy: 0.3456 - loss: 1.4266 - val_accuracy: 0.2088 - val_loss: 1.9354 Epoch 3/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 8s 43ms/step - accuracy: 0.4676 - loss: 1.2488 - val_accuracy: 0.2274 - val_loss: 1.8445 Epoch 4/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 8s 43ms/step - accuracy: 0.5546 - loss: 1.0982 - val_accuracy: 0.2319 - val_loss: 1.7639 Epoch 5/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 9s 44ms/step - accuracy: 0.6234 - loss: 0.9351 - val_accuracy: 0.2844 - val_loss: 1.7828 Epoch 6/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 8s 42ms/step - accuracy: 0.7003 - loss: 0.7959 - val_accuracy: 0.4023 - val_loss: 1.7832 Epoch 7/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 8s 43ms/step - accuracy: 0.7515 - loss: 0.6904 - val_accuracy: 0.4478 - val_loss: 1.8802 Epoch 8/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 8s 43ms/step - accuracy: 0.7787 - loss: 0.6132 - val_accuracy: 0.4888 - val_loss: 1.8402 Epoch 9/50 196/196 ━━━━━━━━━━━━━━━━━━━━ 9s 43ms/step - accuracy: 0.8096 - loss: 0.5284 - val_accuracy: 0.4901 - val_loss: 2.0235
import matplotlib.pyplot as plt
plt.figure(figsize=(20, 6))
# Plot the first subplot (loss)
plt.subplot(1, 2, 1) # 1 row, 2 columns, first subplot
plt.plot(history_model_lstm.history["loss"])
plt.plot(history_model_lstm.history["val_loss"])
plt.title("Validation Loss & Training Loss")
plt.xlabel("Epochs")
plt.ylabel("Loss")
leg = plt.legend(["Training Loss", "Validation Loss"], loc="upper right")
# Plot the second subplot (accuracy)
plt.subplot(1, 2, 2) # 1 row, 2 columns, second subplot
plt.plot(history_model_lstm.history["accuracy"])
plt.plot(history_model_lstm.history["val_accuracy"])
plt.title("Validation Accuracy & Training Accuracy")
plt.xlabel("Epochs")
plt.ylabel("Accuracy")
leg = plt.legend(["Training Accuracy", "Validation Accuracy"], loc="lower right")
plt.show()
So ths deep learning model is overfitted
# dense_array_val_X_val_transformed_reshaped = dense_array_val_X_val_transformed.reshape(dense_array_val_X_val_transformed.shape[0], 1, dense_array_val_X_val_transformed.shape[1])
y_pred_val_model_lstm_normal = model_lstm_normal.predict(dense_array_val_X_val_transformed_reshaped)
57/57 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step
predicted_class_model_lstm_normal = np.argmax(y_pred_val_model_lstm_normal, axis=1)
predicted_class_model_lstm_normal
array([1, 2, 2, ..., 3, 2, 1], dtype=int64)
KAPPA SCORE
from sklearn.metrics import cohen_kappa_score
kp_model_lstm=cohen_kappa_score(y_val,predicted_class_model_lstm_normal)
kp_model_lstm
0.20079267785056332
df_test_copy.head(1)
| Type | Age | Gender | Color1 | Color2 | Color3 | MaturitySize | FurLength | Vaccinated | Dewormed | Sterilized | Health | Fee | Description | Images | Breed | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Cat | 1.0 | Male | Black | White | Unknown | Small | Yes | No | No | No | Healthy | 0.0 | kitten for adoption, pls call for enquiry, off... | C:\Users\praba\Documents\GitHub\UCA SEMESTER 2... | Domestic_Short_Hair |
df_test_copy["Images"][0]
'C:\\Users\\praba\\Documents\\GitHub\\UCA SEMESTER 2 M1\\deeplearning 2\\final project and lab\\drive-download-20240301T144626Z-001\\test_images_all\\5df99d229-2.jpg'
df_test_copy_transformed = my_sklearn_pipeline.transform(df_test_copy)
import pandas as pd
dense_array_test = df_test_copy_transformed.toarray()
df_test_copy_transformed_dataframe = pd.DataFrame(dense_array_test)
df_test_copy_transformed_dataframe.head()
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | 14643 | 14644 | 14645 | 14646 | 14647 | 14648 | 14649 | 14650 | 14651 | 14652 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -1.226519 | -0.559747 | 1.140316 | -1.620921 | -0.310264 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.261236 | 0.070225 | 0.370787 | 0.297753 |
| 1 | 0.815316 | -0.203756 | 1.140316 | 0.207758 | -0.310264 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.337393 | 0.076736 | 0.331303 | 0.254568 |
| 2 | 0.815316 | -0.508891 | -0.876950 | 0.207758 | -0.310264 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.503165 | 0.060127 | 0.164557 | 0.272152 |
| 3 | 0.815316 | -0.458035 | -0.876950 | 0.207758 | -0.310264 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.314961 | 0.031496 | 0.244094 | 0.409449 |
| 4 | -1.226519 | -0.458035 | -0.876950 | 0.207758 | -0.184925 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.440789 | 0.019737 | 0.210526 | 0.328947 |
5 rows × 14653 columns
# df_test_copy_transformed_dataframe.to_csv('df_test_copy_transformed_dataframe_df.csv')
# df_test_copy_transformed_dataframe = pd.read_csv('df_test_copy_transformed_dataframe_df.csv')
# dense_array_test
y_pred_MLP_model2_true = model_dl3.predict(dense_array_test)
16/16 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step
y_pred_MLP_model2_true.shape
(500, 5)
predicted_class_model_model2_test = np.argmax(y_pred_MLP_model2_true, axis=1)
predicted_class_model_model2_test
array([2, 2, 2, 2, 3, 1, 4, 2, 1, 1, 3, 2, 1, 4, 2, 3, 2, 3, 1, 4, 1, 3,
1, 1, 2, 1, 4, 4, 4, 3, 2, 1, 4, 2, 3, 4, 3, 1, 1, 2, 4, 3, 4, 2,
4, 1, 1, 4, 4, 4, 4, 2, 4, 1, 4, 4, 4, 4, 4, 4, 1, 1, 4, 4, 2, 4,
2, 3, 4, 4, 3, 2, 2, 1, 3, 1, 2, 1, 4, 4, 4, 2, 4, 2, 1, 1, 4, 4,
2, 3, 1, 4, 1, 4, 2, 2, 2, 2, 4, 2, 3, 4, 4, 4, 3, 2, 2, 2, 3, 3,
1, 4, 2, 2, 1, 2, 4, 1, 4, 4, 3, 4, 2, 2, 4, 1, 2, 1, 2, 3, 4, 2,
1, 2, 3, 4, 2, 3, 4, 1, 3, 2, 1, 2, 2, 1, 4, 3, 2, 3, 2, 3, 3, 3,
1, 3, 2, 4, 2, 4, 1, 2, 4, 3, 2, 4, 1, 3, 4, 4, 4, 4, 4, 3, 4, 2,
3, 1, 4, 4, 2, 2, 2, 2, 2, 2, 3, 1, 4, 4, 2, 4, 1, 4, 2, 1, 3, 3,
2, 4, 4, 1, 4, 2, 3, 2, 2, 4, 4, 2, 3, 2, 4, 2, 4, 2, 4, 4, 2, 2,
2, 4, 2, 4, 4, 2, 4, 4, 2, 2, 4, 1, 4, 2, 2, 4, 1, 4, 2, 3, 4, 2,
1, 3, 2, 4, 1, 3, 3, 1, 2, 4, 2, 4, 4, 4, 4, 1, 2, 2, 1, 2, 4, 2,
4, 1, 2, 4, 2, 2, 1, 3, 2, 4, 3, 2, 4, 2, 1, 2, 2, 2, 1, 4, 4, 2,
2, 2, 3, 2, 4, 2, 3, 1, 4, 2, 2, 3, 4, 1, 4, 3, 4, 2, 4, 2, 1, 2,
1, 3, 1, 4, 2, 2, 2, 1, 4, 4, 3, 2, 2, 3, 4, 2, 4, 4, 2, 3, 2, 2,
2, 4, 4, 1, 4, 4, 1, 1, 4, 3, 4, 2, 2, 4, 4, 2, 2, 2, 4, 4, 4, 4,
4, 1, 2, 4, 2, 1, 4, 3, 2, 1, 2, 4, 4, 4, 1, 1, 4, 4, 3, 3, 1, 3,
4, 1, 4, 4, 2, 4, 3, 2, 3, 2, 3, 4, 1, 1, 2, 4, 2, 2, 2, 4, 4, 2,
2, 2, 4, 3, 3, 4, 4, 4, 4, 4, 4, 3, 4, 4, 4, 4, 2, 3, 4, 2, 3, 4,
2, 3, 1, 4, 3, 4, 1, 2, 2, 2, 4, 4, 2, 2, 4, 2, 4, 3, 4, 4, 4, 3,
4, 4, 3, 2, 4, 3, 2, 3, 1, 4, 2, 3, 2, 1, 3, 3, 4, 1, 3, 4, 3, 3,
3, 4, 4, 2, 3, 2, 4, 2, 2, 2, 1, 3, 2, 4, 1, 2, 3, 4, 4, 3, 4, 4,
4, 4, 2, 3, 2, 3, 2, 4, 1, 2, 1, 4, 1, 4, 4, 3], dtype=int64)
models_pred_dl = {}
models_pred_dl['prediction rate'] = predicted_class_model_model2_test
df_final_result_test_dl = pd.DataFrame(models_pred_dl)
print(df_final_result_test_dl)
prediction rate 0 2 1 2 2 2 3 2 4 3 .. ... 495 4 496 1 497 4 498 4 499 3 [500 rows x 1 columns]
df_final_result_test_dl.to_csv(' result_dl.csv',index_label = 'id')
__ END _
from keras.preprocessing import image
from keras.applications.vgg16 import preprocess_input
from keras.applications import VGG16
from keras.models import Model
base_model = VGG16(weights='imagenet', include_top=False)
model = Model(inputs=base_model.input, outputs=base_model.output)
import numpy as np
# Function to extract features from a batch of images
def extract_features_batch(img_paths):
batch_features = []
for img_path in img_paths:
img = image.load_img(img_path, target_size=(224, 224)) # Resize image to match VGG16 input size
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x) # Preprocess input based on VGG16 requirements
features = model.predict(x)
batch_features.append(features.flatten()) # Flatten the features into a 1D array
return np.array(batch_features)
# df_train_copy['Images'].shape
df_train_copy['Images'][0]
'C:\\Users\\praba\\Documents\\GitHub\\UCA SEMESTER 2 M1\\deeplearning 2\\final project and lab\\drive-download-20240301T144626Z-001\\train_images_all\\3b178aa59-5.jpg'
df_train_copy['Images'].shape[0]
9000
batch size is 32 used as its not possible to process all 9000 images in a singal batch
# Process images in batches of size 64
batch_size = 32
num_images = df_train_copy['Images'].shape[0]
num_batches = (num_images + batch_size - 1) // batch_size
image_features = []
# Measure the time taken to execute the loop
%time
for batch_index in range(num_batches):
start_index = batch_index * batch_size
end_index = min((batch_index + 1) * batch_size, num_images)
batch_img_paths = df_train_copy['Images'][start_index:end_index]
batch_features = extract_features_batch(batch_img_paths)
image_features.extend(batch_features)
# print("Shape of extracted features:", len(image_features))
CPU times: total: 0 ns Wall time: 0 ns 1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 1s/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 293ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 342ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 302ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 335ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 372ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 279ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 342ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 358ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step 1/1 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━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 342ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 343ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 349ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 392ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 385ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 387ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 387ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 397ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 368ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 373ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 365ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 379ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 350ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 350ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 366ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 334ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 351ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 350ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 350ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 351ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 349ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 343ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 343ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 360ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 357ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 358ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step 1/1 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image_features_array = np.array(image_features)
import pandas as pd
df_vgg = pd.DataFrame(image_features_array)
print("Shape of DataFrame:", df_vgg.shape)
Shape of DataFrame: (9000, 25088)
# Creating pipelines for numerical and categorical features
numerical_pipeline_dl = Pipeline(steps=[
('stdscaler', StandardScaler())
])
categorical_pipeline_dl = Pipeline(steps=[
('onehot', OneHotEncoder(handle_unknown='ignore')) # One-hot encoding categorical features
])
# Define pipeline for text features
text_transformer_pipeline_dl = Pipeline(steps=[ ('vectorizer', CountVectorizer(max_features=1000))])
col_transformer_dl = ColumnTransformer(transformers=[
('drop_columns','drop',drop_features),
('numerical', numerical_pipeline_dl, numerical_features),
('categorical', categorical_pipeline_dl, categorical_features),
('text-data', text_transformer_pipeline_dl, 'Description')
#
])
my_sklearn_pipeline_dl = Pipeline(steps=[
('TypeConverter', TypeConverter()),
('age', AgeConverter()),
('text_cleaner', TextCleaner()),
('MaturitySizeConverter' ,MaturitySizeConverter()),
("GenderConverter",GenderConverter()),
('col_transformer',col_transformer_dl)
]
)
my_sklearn_pipeline_dl
Pipeline(steps=[('TypeConverter', TypeConverter()), ('age', AgeConverter()),
('text_cleaner', TextCleaner()),
('MaturitySizeConverter', MaturitySizeConverter()),
('GenderConverter', GenderConverter()),
('col_transformer',
ColumnTransformer(transformers=[('drop_columns', 'drop',
['Color3']),
('numerical',
Pipeline(steps=[('stdscaler',
StandardScaler())]),
['Type', 'Age', 'Gender',
'MaturitySize', 'Fee']),
('categorical',
Pipeline(steps=[('onehot',
OneHotEncoder(handle_unknown='ignore'))]),
['Color1', 'Color2',
'FurLength', 'Vaccinated',
'Dewormed', 'Sterilized',
'Health', 'Breed']),
('text-data',
Pipeline(steps=[('vectorizer',
CountVectorizer(max_features=1000))]),
'Description')]))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(steps=[('TypeConverter', TypeConverter()), ('age', AgeConverter()),
('text_cleaner', TextCleaner()),
('MaturitySizeConverter', MaturitySizeConverter()),
('GenderConverter', GenderConverter()),
('col_transformer',
ColumnTransformer(transformers=[('drop_columns', 'drop',
['Color3']),
('numerical',
Pipeline(steps=[('stdscaler',
StandardScaler())]),
['Type', 'Age', 'Gender',
'MaturitySize', 'Fee']),
('categorical',
Pipeline(steps=[('onehot',
OneHotEncoder(handle_unknown='ignore'))]),
['Color1', 'Color2',
'FurLength', 'Vaccinated',
'Dewormed', 'Sterilized',
'Health', 'Breed']),
('text-data',
Pipeline(steps=[('vectorizer',
CountVectorizer(max_features=1000))]),
'Description')]))])TypeConverter()
AgeConverter()
TextCleaner()
MaturitySizeConverter()
GenderConverter()
ColumnTransformer(transformers=[('drop_columns', 'drop', ['Color3']),
('numerical',
Pipeline(steps=[('stdscaler',
StandardScaler())]),
['Type', 'Age', 'Gender', 'MaturitySize',
'Fee']),
('categorical',
Pipeline(steps=[('onehot',
OneHotEncoder(handle_unknown='ignore'))]),
['Color1', 'Color2', 'FurLength', 'Vaccinated',
'Dewormed', 'Sterilized', 'Health',
'Breed']),
('text-data',
Pipeline(steps=[('vectorizer',
CountVectorizer(max_features=1000))]),
'Description')])['Color3']
drop
['Type', 'Age', 'Gender', 'MaturitySize', 'Fee']
StandardScaler()
['Color1', 'Color2', 'FurLength', 'Vaccinated', 'Dewormed', 'Sterilized', 'Health', 'Breed']
OneHotEncoder(handle_unknown='ignore')
Description
CountVectorizer(max_features=1000)
X.shape
(9000, 16)
# fits the sklearn pipeline `my_sklearn_pipeline` to the training data X_train.
my_sklearn_pipeline_dl.fit(X)
Pipeline(steps=[('TypeConverter', TypeConverter()), ('age', AgeConverter()),
('text_cleaner', TextCleaner()),
('MaturitySizeConverter', MaturitySizeConverter()),
('GenderConverter', GenderConverter()),
('col_transformer',
ColumnTransformer(transformers=[('drop_columns', 'drop',
['Color3']),
('numerical',
Pipeline(steps=[('stdscaler',
StandardScaler())]),
['Type', 'Age', 'Gender',
'MaturitySize', 'Fee']),
('categorical',
Pipeline(steps=[('onehot',
OneHotEncoder(handle_unknown='ignore'))]),
['Color1', 'Color2',
'FurLength', 'Vaccinated',
'Dewormed', 'Sterilized',
'Health', 'Breed']),
('text-data',
Pipeline(steps=[('vectorizer',
CountVectorizer(max_features=1000))]),
'Description')]))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(steps=[('TypeConverter', TypeConverter()), ('age', AgeConverter()),
('text_cleaner', TextCleaner()),
('MaturitySizeConverter', MaturitySizeConverter()),
('GenderConverter', GenderConverter()),
('col_transformer',
ColumnTransformer(transformers=[('drop_columns', 'drop',
['Color3']),
('numerical',
Pipeline(steps=[('stdscaler',
StandardScaler())]),
['Type', 'Age', 'Gender',
'MaturitySize', 'Fee']),
('categorical',
Pipeline(steps=[('onehot',
OneHotEncoder(handle_unknown='ignore'))]),
['Color1', 'Color2',
'FurLength', 'Vaccinated',
'Dewormed', 'Sterilized',
'Health', 'Breed']),
('text-data',
Pipeline(steps=[('vectorizer',
CountVectorizer(max_features=1000))]),
'Description')]))])TypeConverter()
AgeConverter()
TextCleaner()
MaturitySizeConverter()
GenderConverter()
ColumnTransformer(transformers=[('drop_columns', 'drop', ['Color3']),
('numerical',
Pipeline(steps=[('stdscaler',
StandardScaler())]),
['Type', 'Age', 'Gender', 'MaturitySize',
'Fee']),
('categorical',
Pipeline(steps=[('onehot',
OneHotEncoder(handle_unknown='ignore'))]),
['Color1', 'Color2', 'FurLength', 'Vaccinated',
'Dewormed', 'Sterilized', 'Health',
'Breed']),
('text-data',
Pipeline(steps=[('vectorizer',
CountVectorizer(max_features=1000))]),
'Description')])['Color3']
drop
['Type', 'Age', 'Gender', 'MaturitySize', 'Fee']
StandardScaler()
['Color1', 'Color2', 'FurLength', 'Vaccinated', 'Dewormed', 'Sterilized', 'Health', 'Breed']
OneHotEncoder(handle_unknown='ignore')
Description
CountVectorizer(max_features=1000)
X_train_transform_dl = my_sklearn_pipeline_dl.transform(X)
X_train_transform_dl
<9000x1187 sparse matrix of type '<class 'numpy.float64'>' with 321553 stored elements in Compressed Sparse Row format>
import pandas as pd
dense_arrayX_train_transform_dl = X_train_transform_dl.toarray()
df_dense_arrayX_train_transform_dl = pd.DataFrame(dense_arrayX_train_transform_dl)
df_dense_arrayX_train_transform_dl.head(2)
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | 1177 | 1178 | 1179 | 1180 | 1181 | 1182 | 1183 | 1184 | 1185 | 1186 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.820282 | 3.720374 | 1.129934 | -1.626893 | -0.299511 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1 | 0.820282 | -0.557090 | -0.885007 | 0.208409 | 0.313454 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
2 rows × 1187 columns
from sklearn.pipeline import Pipeline
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler, MinMaxScaler
pipeline_final_pca = Pipeline(steps=[
# ('pipe_my',my_sklearn_pipeline),
('scaler', MinMaxScaler()), # Step 1: MinMaxScaler the features
('pca', PCA(n_components=5)) # Step 5: Apply PCA with 2 components
],
memory = 'tmp/cache'
)
pipeline_final_pca
Pipeline(memory='tmp/cache',
steps=[('scaler', MinMaxScaler()), ('pca', PCA(n_components=5))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(memory='tmp/cache',
steps=[('scaler', MinMaxScaler()), ('pca', PCA(n_components=5))])MinMaxScaler()
PCA(n_components=5)
# Now you can fit the pipeline to your data and transform it
# Assuming X is your data matrix
pca_result = pipeline_final_pca.fit_transform(df_vgg)
pca_result.shape
(9000, 5)
import pandas as pd
df_vgg_pca = pd.DataFrame(pca_result)
print("Shape of DataFrame:", df_vgg_pca.shape)
Shape of DataFrame: (9000, 5)
result_final_data = pd.concat([df_dense_arrayX_train_transform_dl, df_vgg_pca,y], axis=1)
result_final_data.shape
(9000, 1193)
result_final_data.head(2)
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | 1183 | 1184 | 1185 | 1186 | 0 | 1 | 2 | 3 | 4 | AdoptionSpeed | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.820282 | 3.720374 | 1.129934 | -1.626893 | -0.299511 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 1.013023 | -0.589681 | 0.710931 | 0.651019 | -1.138258 | 4.0 |
| 1 | 0.820282 | -0.557090 | -0.885007 | 0.208409 | 0.313454 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 1.0 | 0.0 | 1.744552 | 0.926937 | -1.067492 | 0.113089 | -0.893152 | 3.0 |
2 rows × 1193 columns
import pandas as pd
result_final_data.columns = range(len(result_final_data.columns))
print(result_final_data)
0 1 2 3 4 5 6 7 \
0 0.820282 3.720374 1.129934 -1.626893 -0.299511 0.0 1.0 0.0
1 0.820282 -0.557090 -0.885007 0.208409 0.313454 1.0 0.0 0.0
2 0.820282 -0.557090 1.129934 0.208409 -0.299511 0.0 1.0 0.0
3 0.820282 -0.454018 1.129934 0.208409 -0.299511 1.0 0.0 0.0
4 0.820282 -0.196340 1.129934 2.043710 -0.299511 0.0 1.0 0.0
... ... ... ... ... ... ... ... ...
8995 0.820282 1.298196 -0.885007 0.208409 -0.299511 0.0 0.0 1.0
8996 -1.219093 -0.299411 1.129934 0.208409 -0.299511 0.0 0.0 0.0
8997 -1.219093 -0.350947 1.129934 -1.626893 -0.299511 1.0 0.0 0.0
8998 0.820282 0.009803 1.129934 0.208409 -0.299511 0.0 1.0 0.0
8999 0.820282 -0.505554 -0.885007 0.208409 -0.299511 1.0 0.0 0.0
8 9 ... 1183 1184 1185 1186 1187 1188 1189 \
0 0.0 0.0 ... 0.0 0.0 0.0 0.0 1.013023 -0.589681 0.710931
1 0.0 0.0 ... 0.0 0.0 1.0 0.0 1.744552 0.926937 -1.067492
2 0.0 0.0 ... 0.0 0.0 0.0 0.0 2.968973 -0.419956 0.513002
3 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.303877 -0.815112 0.252377
4 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.616000 -0.269426 0.987940
... ... ... ... ... ... ... ... ... ... ...
8995 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.310028 -0.752743 0.259324
8996 0.0 0.0 ... 0.0 0.0 0.0 0.0 -0.815235 0.466691 -1.140541
8997 0.0 0.0 ... 0.0 0.0 0.0 0.0 -0.966591 0.645182 2.310626
8998 0.0 0.0 ... 0.0 0.0 1.0 0.0 2.535786 -1.612642 -0.228553
8999 0.0 0.0 ... 0.0 0.0 0.0 0.0 1.669739 -0.084968 -1.070211
1190 1191 1192
0 0.651019 -1.138258 4.0
1 0.113089 -0.893152 3.0
2 -1.587275 -0.142300 1.0
3 1.002717 -0.438310 4.0
4 1.800154 -0.814048 3.0
... ... ... ...
8995 1.071156 0.724100 2.0
8996 -1.323398 -0.551580 4.0
8997 -0.348627 -0.028501 3.0
8998 -0.937576 -1.578858 4.0
8999 0.254605 0.259421 1.0
[9000 rows x 1193 columns]
result_final_data.head()
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | 1183 | 1184 | 1185 | 1186 | 1187 | 1188 | 1189 | 1190 | 1191 | 1192 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.820282 | 3.720374 | 1.129934 | -1.626893 | -0.299511 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 1.013023 | -0.589681 | 0.710931 | 0.651019 | -1.138258 | 4.0 |
| 1 | 0.820282 | -0.557090 | -0.885007 | 0.208409 | 0.313454 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 1.0 | 0.0 | 1.744552 | 0.926937 | -1.067492 | 0.113089 | -0.893152 | 3.0 |
| 2 | 0.820282 | -0.557090 | 1.129934 | 0.208409 | -0.299511 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 2.968973 | -0.419956 | 0.513002 | -1.587275 | -0.142300 | 1.0 |
| 3 | 0.820282 | -0.454018 | 1.129934 | 0.208409 | -0.299511 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.303877 | -0.815112 | 0.252377 | 1.002717 | -0.438310 | 4.0 |
| 4 | 0.820282 | -0.196340 | 1.129934 | 2.043710 | -0.299511 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.616000 | -0.269426 | 0.987940 | 1.800154 | -0.814048 | 3.0 |
5 rows × 1193 columns
result_final_data_X = result_final_data.drop(1192, axis=1).copy()
result_final_data_y = result_final_data[1192]
from sklearn.model_selection import train_test_split
result_final_dataX_train, result_final_dataX_val, result_final_datay_train, result_final_datay_val = train_test_split(result_final_data_X, result_final_data_y, test_size=0.10, random_state=2022,stratify=result_final_data_y)
# import numpy as np
# result_final_data_X_array = result_final_data_X.values
# result_final_data_y_array = result_final_data_y.values
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Input, Dense, Dropout, BatchNormalization
from tensorflow.keras import regularizers
model_dl_mlp_4 = Sequential([
Input(shape=(result_final_dataX_train.shape[1],)),
Dense(1024, activation='relu', kernel_regularizer=regularizers.l2(0.003)),
BatchNormalization(),
Dropout(0.3),
Dense(512, activation='relu', kernel_regularizer=regularizers.l2(0.003)),
BatchNormalization(),
Dropout(0.3),
Dense(256, activation='relu', kernel_regularizer=regularizers.l2(0.003)),
BatchNormalization(),
Dropout(0.3),
Dense(128, activation='relu', kernel_regularizer=regularizers.l2(0.003)),
BatchNormalization(),
Dropout(0.3),
Dense(64, activation='relu', kernel_regularizer=regularizers.l2(0.003)),
BatchNormalization(),
Dropout(0.3),
Dense(32, activation='relu', kernel_regularizer=regularizers.l2(0.003)),
BatchNormalization(),
Dense(5, activation='softmax')
])
# from tensorflow.keras.utils import plot_model
# plot_model(model3, to_file='model_plot4.png', show_shapes=True, show_laye
# from tensorflow.keras.utils import plot_model
from keras.utils import plot_model
# plot_model(model3, to_file='model_plot4.png', show_shapes=True, show_laye
plot_model(model_dl_mlp_4, to_file='model_dl_mlp_4.png',
show_shapes=True,
show_layer_names=True,
layer_range=None,
show_layer_activations=True,
show_trainable=True)
model_dl_mlp_4.summary()
Model: "sequential_22"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ dense_134 (Dense) │ (None, 1024) │ 1,221,632 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ batch_normalization_84 │ (None, 1024) │ 4,096 │ │ (BatchNormalization) │ │ │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_103 (Dropout) │ (None, 1024) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_135 (Dense) │ (None, 512) │ 524,800 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ batch_normalization_85 │ (None, 512) │ 2,048 │ │ (BatchNormalization) │ │ │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_104 (Dropout) │ (None, 512) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_136 (Dense) │ (None, 256) │ 131,328 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ batch_normalization_86 │ (None, 256) │ 1,024 │ │ (BatchNormalization) │ │ │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_105 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_137 (Dense) │ (None, 128) │ 32,896 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ batch_normalization_87 │ (None, 128) │ 512 │ │ (BatchNormalization) │ │ │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_106 (Dropout) │ (None, 128) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_138 (Dense) │ (None, 64) │ 8,256 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ batch_normalization_88 │ (None, 64) │ 256 │ │ (BatchNormalization) │ │ │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_107 (Dropout) │ (None, 64) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_139 (Dense) │ (None, 32) │ 2,080 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ batch_normalization_89 │ (None, 32) │ 128 │ │ (BatchNormalization) │ │ │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_140 (Dense) │ (None, 5) │ 165 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 5,779,601 (22.05 MB)
Trainable params: 1,925,189 (7.34 MB)
Non-trainable params: 4,032 (15.75 KB)
Optimizer params: 3,850,380 (14.69 MB)
model_dl_mlp_4.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
from tensorflow.keras.callbacks import EarlyStopping
early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)
history_model_mlp4 = model_dl_mlp_4.fit(result_final_dataX_train, result_final_datay_train, epochs=50, validation_split=0.2, batch_size=64, callbacks=[early_stopping])
Epoch 1/50 102/102 ━━━━━━━━━━━━━━━━━━━━ 11s 45ms/step - accuracy: 0.2474 - loss: 8.4722 - val_accuracy: 0.2790 - val_loss: 7.5343 Epoch 2/50 102/102 ━━━━━━━━━━━━━━━━━━━━ 4s 39ms/step - accuracy: 0.3105 - loss: 7.4171 - val_accuracy: 0.3142 - val_loss: 6.7200 Epoch 3/50 102/102 ━━━━━━━━━━━━━━━━━━━━ 4s 38ms/step - accuracy: 0.3254 - loss: 6.5251 - val_accuracy: 0.3605 - val_loss: 5.8606 Epoch 4/50 102/102 ━━━━━━━━━━━━━━━━━━━━ 4s 39ms/step - accuracy: 0.3977 - loss: 5.6452 - val_accuracy: 0.3481 - val_loss: 5.1123 Epoch 5/50 102/102 ━━━━━━━━━━━━━━━━━━━━ 4s 37ms/step - accuracy: 0.4130 - loss: 4.8705 - val_accuracy: 0.3716 - val_loss: 4.4691 Epoch 6/50 102/102 ━━━━━━━━━━━━━━━━━━━━ 4s 38ms/step - accuracy: 0.4628 - loss: 4.2167 - val_accuracy: 0.3728 - val_loss: 3.9810 Epoch 7/50 102/102 ━━━━━━━━━━━━━━━━━━━━ 4s 39ms/step - accuracy: 0.5015 - loss: 3.6573 - val_accuracy: 0.3889 - val_loss: 3.5807 Epoch 8/50 102/102 ━━━━━━━━━━━━━━━━━━━━ 4s 37ms/step - accuracy: 0.5444 - loss: 3.2121 - val_accuracy: 0.4006 - val_loss: 3.2764 Epoch 9/50 102/102 ━━━━━━━━━━━━━━━━━━━━ 4s 36ms/step - accuracy: 0.5857 - loss: 2.8331 - val_accuracy: 0.3778 - val_loss: 3.1503 Epoch 10/50 102/102 ━━━━━━━━━━━━━━━━━━━━ 4s 38ms/step - accuracy: 0.6125 - loss: 2.5850 - val_accuracy: 0.3722 - val_loss: 3.0350 Epoch 11/50 102/102 ━━━━━━━━━━━━━━━━━━━━ 4s 39ms/step - accuracy: 0.6469 - loss: 2.3746 - val_accuracy: 0.3506 - val_loss: 2.9993 Epoch 12/50 102/102 ━━━━━━━━━━━━━━━━━━━━ 4s 37ms/step - accuracy: 0.6535 - loss: 2.2685 - val_accuracy: 0.3679 - val_loss: 2.9741 Epoch 13/50 102/102 ━━━━━━━━━━━━━━━━━━━━ 4s 37ms/step - accuracy: 0.7005 - loss: 2.1463 - val_accuracy: 0.3864 - val_loss: 2.9244 Epoch 14/50 102/102 ━━━━━━━━━━━━━━━━━━━━ 4s 38ms/step - accuracy: 0.7250 - loss: 2.0307 - val_accuracy: 0.3827 - val_loss: 2.9263 Epoch 15/50 102/102 ━━━━━━━━━━━━━━━━━━━━ 4s 36ms/step - accuracy: 0.7371 - loss: 2.0019 - val_accuracy: 0.3864 - val_loss: 2.9368 Epoch 16/50 102/102 ━━━━━━━━━━━━━━━━━━━━ 4s 38ms/step - accuracy: 0.7580 - loss: 1.9289 - val_accuracy: 0.3741 - val_loss: 3.0698 Epoch 17/50 102/102 ━━━━━━━━━━━━━━━━━━━━ 4s 37ms/step - accuracy: 0.7570 - loss: 1.9125 - val_accuracy: 0.3796 - val_loss: 3.0078 Epoch 18/50 102/102 ━━━━━━━━━━━━━━━━━━━━ 4s 37ms/step - accuracy: 0.7904 - loss: 1.8507 - val_accuracy: 0.3895 - val_loss: 2.9358
import matplotlib.pyplot as plt
plt.figure(figsize=(20, 6))
# Plot the first subplot (loss)
plt.subplot(1, 2, 1) # 1 row, 2 columns, first subplot
plt.plot(history_model_mlp4.history["loss"])
plt.plot(history_model_mlp4.history["val_loss"])
plt.title("Validation Loss & Training Loss")
plt.xlabel("Epochs")
plt.ylabel("Loss")
leg = plt.legend(["Training Loss", "Validation Loss"], loc="upper right")
# Plot the second subplot (accuracy)
plt.subplot(1, 2, 2) # 1 row, 2 columns, second subplot
plt.plot(history_model_mlp4.history["accuracy"])
plt.plot(history_model_mlp4.history["val_accuracy"])
plt.title("Validation Accuracy & Training Accuracy")
plt.xlabel("Epochs")
plt.ylabel("Accuracy")
leg = plt.legend(["Training Accuracy", "Validation Accuracy"], loc="lower right")
plt.show()
y_pred_val_model_mlp4 = model_dl_mlp_4.predict(result_final_dataX_val)
29/29 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step
predicted_class_model_mlp4 = np.argmax(y_pred_val_model_mlp4, axis=1)
predicted_class_model_mlp4.shape
(900,)
from sklearn.metrics import cohen_kappa_score
kp_model_mlp4=cohen_kappa_score(result_final_datay_val,predicted_class_model_mlp4)
kp_model_mlp4
0.1875805825369964